Output:
Exit code: 0
Wall time: 2 seconds
Output:
cols ['video_id', 'found_in_db', 'source_lang', 'classification_label', 'podcast_probability', 'music_contamination_risk', 'channel_id', 'title', 'duration_seconds', 'source_keyword', 'discovered_at', 'pre_filter_passed', 'ai_classified', 'chapters_count', 'chapters_source', 'intro_end_seconds', 'chapters_fetched_at', 'chapters_error']
   video_id  found_in_db source_lang classification_label  podcast_probability  music_contamination_risk               channel_id                                                                                title  duration_seconds                   source_keyword       discovered_at  pre_filter_passed  ai_classified  chapters_count chapters_source  intro_end_seconds chapters_fetched_at  chapters_error
cS8Uwm8YAlw            1          ta         podcast_like                  1.0                       NaN UCaYXNPf-LWB545qzV6rA9wQ              Why is Egypt Mysterious? 🏺 - Tamil Podcast | Paari Saalan | Vallal Media | Varun Talks              3210 channel:UCaYXNPf-LWB545qzV6rA9wQ 2026-01-09 03:32:53                  1              1               0            none                NaN 2026-01-11 05:28:47             NaN
dRNOoagxnc0            1          hi         podcast_like                  1.0                       NaN UCkJZQddO3XTCdcLjN019FjA Jugal Hansraj on Mohabbatein, Bond with Karan Johar, Aditya Chopra & Bollywood Rejections | Podcast              4059                              NaN 2026-01-09 06:36:49                  1              1              20          yt_dlp                NaN 2026-01-11 05:28:47             NaN
dZUYjjgnWCQ            1          hi         podcast_like                  1.0                       NaN UCkJZQddO3XTCdcLjN019FjA  Sukhwinder Singh on Jai Ho, AR Rahman, Shah Rukh Khan, Chak De India, Gulzar Saab & More | Podcast              3784                              NaN 2026-01-09 06:36:49                  1              1              21          yt_dlp               96.0 2026-01-11 05:28:47             NaN


Summary: auto

## Skills
A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and file path so you can open the source for full instructions when using a specific skill.
### Available skills
- skill-creator: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations. (file: /home/ubuntu/.codex/skills/.system/skill-creator/SKILL.md)
- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: /home/ubuntu/.codex/skills/.system/skill-installer/SKILL.md)
### How to use skills
- Discovery: The list above is the skills available in this session (name + description + file path). Skill bodies live on disk at the listed paths.
- Trigger rules: If the user names a skill (with `$SkillName` or plain text) OR the task clearly matches a skill's description shown above, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned.
- Missing/blocked: If a named skill isn't in the list or the path can't be read, say so briefly and continue with the best fallback.
- How to use a skill (progressive disclosure):
  1) After deciding to use a skill, open its `SKILL.md`. Read only enough to follow the workflow.
  2) If `SKILL.md` points to extra folders such as `references/`, load only the specific files needed for the request; don't bulk-load everything.
  3) If `scripts/` exist, prefer running or patching them instead of retyping large code blocks.
  4) If `assets/` or templates exist, reuse them instead of recreating from scratch.
- Coordination and sequencing:
  - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them.
  - Announce which skill(s) you're using and why (one short line). If you skip an obvious skill, say why.
- Context hygiene:
  - Keep context small: summarize long sections instead of pasting them; only load extra files when needed.
  - Avoid deep reference-chasing: prefer opening only files directly linked from `SKILL.md` unless you're blocked.
  - When variants exist (frameworks, providers, domains), pick only the relevant reference file(s) and note that choice.
- Safety and fallback: If a skill can't be applied cleanly (missing files, unclear instructions), state the issue, pick the next-best approach, and continue.


**Exploring podcast data**

I'm looking at `new_podcasts.csv`, which shares similar columns, and it has a `found_in_db` field. Then I see that `discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv` is a subset with 9 columns. I suspect their videos table includes these discovered metadata, possibly under `extra_meta`. It seems I need to search for instances of where `new_podcasts` is being used, so I’ll use something like `rg new_podcasts.csv`. Let's see what I find!


rg -n "new_podcasts\.csv|discovered_podcasts" -S .


Output:
Exit code: 1
Wall time: 0.1 seconds
Output:


Summary: auto


**Evaluating data insertion methods**

I need counts for language — maybe checking for `language eq Telugu`? We’re focused on analytics for the CSV. For insertion, using SupabaseClient.bulk_upsert could work for merging duplicates, but I don’t want to overwrite statuses. If I only insert new IDs, it should be safe since duplicates won’t be present. However, it’s tricky with status. Maybe avoiding status in the payload for existing records could help, but then I can’t guarantee that. So, perhaps I should just insert new IDs using POST instead of upsert?


**Planning data insertion approach**

I think the best approach in this script is to prefilter for new IDs and use `bulk_upsert` with a payload that includes the status PENDING. This should be fine since I’m only adding new entries. However, I worry about possible duplicates if the database changes between checks and inserts. Instead, I could use insert with "Prefer: resolution=ignore-duplicates.” Supabase docs suggest using "resolution=merge-duplicates" for upserts and "ignore-duplicates" for simple inserts. I’ll implement this in the script to handle new rows efficiently.


**Enhancing SupabaseClient for inserts**

I realize that the SupabaseClient doesn’t have the method I need, but I could implement it by calling `client._request` with headers to ignore duplicates. Even though `_request` is an internal method, it seems accessible. However, I think it might be better to add a dedicated method called `bulk_insert_ignore_duplicates` to keep things clean. I also need to set the IDs for inserted entries to pending, but what about existing IDs with different statuses, like SKIPPED? The user did mention setting all videoIDs to pending, but I wonder if that includes other statuses too?


**Clarifying video ID statuses**

I’m wondering if the user wants to reset completed IDs to pending, but that feels like it would double the work, which seems unlikely. From their request to "set all videoIDs to pending," it sounds like they want those IDs in the pending queue for workers. If some already exist but aren’t pending, like if they were 'SKIPPED,' it's possible they just want everything in the CSV to be pending except for failed ones. I should check the "MAYA3 pipeline complete analytics report" to see the counts of pending and failed video IDs.


**Planning new insertions and counts**

Adding pending status for new IDs seems good! For each ID in the CSV, I’ll insert it with status PENDING if it’s not already in the table. If it is in the table and the status is FAILED, I’ll keep it as is. I need to consider adding metadata for new Telugu videoIDs too. I'll map fields accordingly and prefer using 'Telugu' for the language.

To verify, I can run a query in the database to check counts for 'Telugu' and 'te'. If I have access to psql, I can execute those SQL queries directly. I'll check for installation first!


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
/bin/bash: line 1: psql: command not found


**Checking Python packages**

I need to connect with a database, but it looks like I don’t have psql set up. So, I’m thinking about using Python with the psycopg2 library, but I’m not sure if it’s installed. Alternatively, I could use supabase-py, though that might not be in my system environment either. I guess I’ll try running a quick command to check the version of supabase. Let’s see what happens!


python3 - <<'PY'
try:
    import supabase
    print('supabase installed', supabase.__version__)
except Exception as e:
    print('no supabase', e)
PY


Output:
Exit code: 0
Wall time: 3.6 seconds
Output:
supabase installed 2.27.0


python3 - <<'PY'
from supabase import create_client
import inspect
from postgrest import SyncRequestBuilder
# We can't easily access builder methods without client, but we can inspect class
import postgrest
from postgrest import _sync
from postgrest._sync.request_builder import SyncRequestBuilder
print('SyncRequestBuilder methods:', [m for m in dir(SyncRequestBuilder) if m in ['insert','upsert','select','update','delete']])
print('insert sig', inspect.signature(SyncRequestBuilder.insert))
print('upsert sig', inspect.signature(SyncRequestBuilder.upsert))
PY


Output:
Exit code: 0
Wall time: 2.9 seconds
Output:
SyncRequestBuilder methods: ['delete', 'insert', 'select', 'update', 'upsert']
insert sig (self, json: 'JSON', *, count: 'Optional[CountMethod]' = None, returning: 'ReturnMethod' = <ReturnMethod.representation: 'representation'>, upsert: 'bool' = False, default_to_null: 'bool' = True) -> 'SyncQueryRequestBuilder'
upsert sig (self, json: 'JSON', *, count: 'Optional[CountMethod]' = None, returning: 'ReturnMethod' = <ReturnMethod.representation: 'representation'>, ignore_duplicates: 'bool' = False, on_conflict: 'str' = '', default_to_null: 'bool' = True) -> 'SyncQueryRequestBuilder'


**Evaluating upsert function**

I’ve noticed that the upsert function has an `ignore_duplicates` parameter, which is fantastic! We can set it to `True` with `on_conflict='youtube_id'` to insert new entries while ignoring existing ones, which is just what we need. But will it only ignore duplicates in cases of conflict? Yes, that seems to be the case.

Still, to track what's new versus existing, I plan to prefetch existing statuses for the IDs before the upsert. I'll calculate the inserted count, considering potential failures due to invalid inputs.


**Clarifying upsert counts**

I'm thinking about the `upsert` function, particularly regarding `ignore_duplicates`. It seems like the `count` might return the number of rows inserted. I’m curious whether asking for `count='exact'` would show the count of inserted or updated rows, which could include duplicates.

To confirm, I'll query the existing count both before and after the operation. I'll calculate the successful inserts using those figures. Additionally, I need all video IDs to have their status set to "pending," and I must check the previous session log for reference to similar operations related to Supabase analytics.


rg -n "sync|csv|UNIQUE|pending|FAILED|retry" 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt | head -n 80


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
296:❯ and then now can you also sync the current workers with the database and
311:       File "/home/ubuntu/.local/lib/python3.10/site-packages/postgrest/_sync/req
409:  │ FAILED    │ 13,655  │
464:  failure reason. maybe reset them back to pending so our current workers
469:  ⎿ === FAILED VIDEOS BREAKDOWN ===
482:● Found 640 retryable failed videos:
490:  ⎿  Reset 640 videos from FAILED to PENDING
495:● Done. Reset 640 videos from FAILED to PENDING.
505:  │ FAILED    │ 15,862 (remaining are R2 source not found) │
509:  Workers will now start claiming and processing these 640 retryable videos.


sed -n '360,540p' 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt


Output:
Exit code: 0
Wall time: 0 seconds
Output:

❯ <ide_opened_file>The user opened the file /home/ubuntu/maya3/.env in the
  IDE. This may or may not be related to the current
  task.</ide_opened_file>

❯ can you now check with supabase and give the analysis of completed
  videos, total processed vs cleaned number of hours available per language
  analysis and etc.

● Bash(python3 -c "                    timeout: 30s
      from dotenv import load_dotenv…)
  ⎿  === COMPLETED VIDEOS ANALYSIS ===

● Bash(python3 -c "                    timeout: 1m 0s
      from dotenv import load_dotenv…)
  ⎿  Error: Exit code 1
     Traceback (most recent call last):
       File "<string>", line 12, in <module>
     psycopg2.errors.UndefinedColumn: column "usable_audio_sec" does not exist
     LINE 5:         SUM(usable_audio_sec) / 3600.0 as hours
                         ^

● Bash(python3 -c "                    timeout: 30s
      from dotenv import load_dotenv…)
  ⎿  === VIDEOS TABLE COLUMNS ===
       id: uuid
       youtube_id: character varying
     … +46 lines (ctrl+o to expand)

● Bash(python3 -c "                    timeout: 1m 0s
      from dotenv import load_dotenv…)
  ⎿  === TODAY'S PROCESSING ===
     Completed today: 99,893
     Usable hours today: 35,036.7
     … +6 lines (ctrl+o to expand)

● Here's the full pipeline analysis:

  ---
  MAYA3 PIPELINE ANALYTICS

  Video Status Breakdown
  ┌───────────┬─────────┐
  │  Status   │  Count  │
  ├───────────┼─────────┤
  │ COMPLETED │ 326,138 │
  ├───────────┼─────────┤
  │ PENDING   │ 147,663 │
  ├───────────┼─────────┤
  │ FAILED    │ 13,655  │
  ├───────────┼─────────┤
  │ CLAIMED   │ 374     │
  ├───────────┼─────────┤
  │ TOTAL     │ 487,830 │
  └───────────┴─────────┘
  Usable Audio (Completed Videos)

  - Videos with usable audio: 326,139
  - Total usable hours: 99,816 hrs
  - Total source audio hours: 157,708 hrs
  - Avg usable percentage: 61.9%

  R2 Uploads

  - Videos uploaded: 326,158
  - Total size: 13,683 GB (13.4 TB)

  Hours by Language (Top Languages)
  ┌───────────────┬────────┬────────────┐
  │   Language    │ Videos │ Usable Hrs │
  ├───────────────┼────────┼────────────┤
  │ unknown       │ 64,664 │ 19,707     │
  ├───────────────┼────────┼────────────┤
  │ hindi         │ 30,235 │ 9,214      │
  ├───────────────┼────────┼────────────┤
  │ tamil         │ 37,907 │ 7,895      │
  ├───────────────┼────────┼────────────┤
  │ hi            │ 26,213 │ 7,544      │
  ├───────────────┼────────┼────────────┤
  │ punjabi       │ 16,051 │ 5,833      │
  ├───────────────┼────────┼────────────┤
  │ telugu        │ 22,468 │ 5,716      │
  ├───────────────┼────────┼────────────┤
  │ gujarati (gu) │ 7,962  │ 4,682      │
  ├───────────────┼────────┼────────────┤
  │ kannada (kn)  │ 8,380  │ 4,458      │
  ├───────────────┼────────┼────────────┤
  │ malayalam     │ 16,686 │ 4,334      │
  ├───────────────┼────────┼────────────┤
  │ bengali (bn)  │ 13,170 │ 3,746      │
  └───────────────┴────────┴────────────┘
  Today's Processing

  - Completed today: 99,893 videos
  - Usable hours today: 35,037 hrs
  - Last hour: 7,486 videos / 3,695 hrs

  Failed Videos

  - DownloadError: 13,427
  - RuntimeError: 230

❯ i need you to filter the failed items in supabase and check the items
  that are failed "not because of R2 source not found" and has a different
  failure reason. maybe reset them back to pending so our current workers
  will start claiming them and process those ones too.

● Bash(python3 -c "                    timeout: 1m 0s
      from dotenv import load_dotenv…)
  ⎿ === FAILED VIDEOS BREAKDOWN ===
    Error Type              Count  Error Preview
    ----------------------------------------------------------------------
    -
    … +61 lines (ctrl+o to expand)

● Bash(python3 -c "                    timeout: 1m 0s
      from dotenv import load_dotenv…)
  ⎿  Retryable failed videos (not R2 source not found): 640

     === RETRYABLE ERRORS BREAKDOWN ===
     … +911 lines (ctrl+o to expand)

● Found 640 retryable failed videos:
  - 185 ffmpeg processing errors
  - ~455 R2 upload failures (not source not found)

  Resetting them to PENDING:

● Bash(python3 -c "                    timeout: 1m 0s
      from dotenv import load_dotenv…)
  ⎿  Reset 640 videos from FAILED to PENDING

     === UPDATED STATUS COUNTS ===
     … +4 lines (ctrl+o to expand)

● Done. Reset 640 videos from FAILED to PENDING.

  Updated status:
  ┌───────────┬────────────────────────────────────────────┐
  │  Status   │                   Count                    │
  ├───────────┼────────────────────────────────────────────┤
  │ COMPLETED │ 459,518                                    │
  ├───────────┼────────────────────────────────────────────┤
  │ PENDING   │ 11,949                                     │
  ├───────────┼────────────────────────────────────────────┤
  │ FAILED    │ 15,862 (remaining are R2 source not found) │
  ├───────────┼────────────────────────────────────────────┤
  │ CLAIMED   │ 501                                        │
  └───────────┴────────────────────────────────────────────┘
  Workers will now start claiming and processing these 640 retryable videos.

❯ can you check if there are more instances that i can deploy to. like
  norway ones or US ones. especially with high internet ?

● Bash(python3 -c " timeout: 1m 0s
      import os…)
  ⎿ === HIGH-SPEED NORWAY/US OFFERS (37 found) ===
    Offer        GPUs               $/hr     Down       Up Location
    ----------------------------------------------------------------------
    -
    … +43 lines (ctrl+o to expand)

● Found some great options:

  Norway (High Speed, Affordable)
  ┌──────────┬────────────────┬───────┬─────────────────┐
  │  Offer   │      GPUs      │ $/hr  │ Speed (Down/Up) │
  ├──────────┼────────────────┼───────┼─────────────────┤
  │ 29204632 │ 8x RTX 5060 Ti │ $0.86 │ 8703/7231 Mbps  │
  ├──────────┼────────────────┼───────┼─────────────────┤
  │ 29644702 │ 8x RTX 4060 Ti │ $0.86 │ 8514/7296 Mbps  │
  ├──────────┼────────────────┼───────┼─────────────────┤
  │ 28907816 │ 8x RTX 4090    │ $3.20 │ 4621/5576 Mbps  │
  ├──────────┼────────────────┼───────┼─────────────────┤
  │ 29262135 │ 8x RTX 4090    │ $3.20 │ 4473/4564 Mbps  │
  ├──────────┼────────────────┼───────┼─────────────────┤
  │ 28691942 │ 12x RTX 4090   │ $4.80 │ 4285/2581 Mbps  │
  ├──────────┼────────────────┼───────┼─────────────────┤
  │ 28017397 │ 12x RTX 4090   │ $4.80 │ 4155/4669 Mbps  │
  └──────────┴────────────────┴───────┴─────────────────┘
  US (High Speed)


rg -n "=== VIDEOS TABLE COLUMNS ===" -n 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
384:  ⎿  === VIDEOS TABLE COLUMNS ===


sed -n '380,420p' 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt


Output:
Exit code: 0
Wall time: 0 seconds
Output:
                         ^

● Bash(python3 -c "                    timeout: 30s
      from dotenv import load_dotenv…)
  ⎿  === VIDEOS TABLE COLUMNS ===
       id: uuid
       youtube_id: character varying
     … +46 lines (ctrl+o to expand)

● Bash(python3 -c "                    timeout: 1m 0s
      from dotenv import load_dotenv…)
  ⎿  === TODAY'S PROCESSING ===
     Completed today: 99,893
     Usable hours today: 35,036.7
     … +6 lines (ctrl+o to expand)

● Here's the full pipeline analysis:

  ---
  MAYA3 PIPELINE ANALYTICS

  Video Status Breakdown
  ┌───────────┬─────────┐
  │  Status   │  Count  │
  ├───────────┼─────────┤
  │ COMPLETED │ 326,138 │
  ├───────────┼─────────┤
  │ PENDING   │ 147,663 │
  ├───────────┼─────────┤
  │ FAILED    │ 13,655  │
  ├───────────┼─────────┤
  │ CLAIMED   │ 374     │
  ├───────────┼─────────┤
  │ TOTAL     │ 487,830 │
  └───────────┴─────────┘
  Usable Audio (Completed Videos)

  - Videos with usable audio: 326,139
  - Total usable hours: 99,816 hrs
  - Total source audio hours: 157,708 hrs
  - Avg usable percentage: 61.9%


**Checking column accessibility**

I’m looking into the columns listed in the repository, but it seems they are truncated and not accessible. I wonder if I can find them in the current repository scripts, like `scripts/run_analytics_migration.py`. That might help me fetch the column information.

For our insertion, I have a few columns in mind: youtube_id, youtube_url, status, created_at, updated_at, title, source_duration_min, and possibly channel. I realize that while the channel field expects the channel name, I can store the channel_id in the extra_meta—it's not critical, but it’ll help for analysis. I'll need to verify how the download code depends on chapters and title from Supabase. I'll also look into `src/download.py` to clarify how chapters are used.


rg -n "supabase_metadata|get\('chapters'|chapter_count|has_chapters|extra_meta" src/download.py | head -n 80


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
127:                    'chapters': info.get('chapters') or [],
580:    supabase_metadata: Dict[str, Any] = None,
712:    if supabase_metadata:
713:        video_title = supabase_metadata.get('title') or video_title
714:        chapters = supabase_metadata.get('chapters') or []
715:        has_chapters = supabase_metadata.get('has_chapters', False)
716:        source_duration = supabase_metadata.get('source_duration_min', 0) * 60 if supabase_metadata.get('source_duration_min') else actual_duration
718:        if chapters and has_chapters:
876:    sb_chapters = (supabase_metadata.get('chapters') or []) if supabase_metadata else []
954:    supabase_metadata: Dict[str, Any] = None,
971:    When supabase_metadata is provided (from distributed queue), use it for:
980:        supabase_metadata: Pre-fetched metadata from Supabase (chapters, title, etc.)
1022:            supabase_metadata=supabase_metadata,
1044:            supabase_metadata=supabase_metadata,
1122:                        'chapters': info.get('chapters', []),
1140:    chapters = info.get('chapters', []) if info else []
1428:        'chapters': info.get('chapters', []) if info else [],
1578:    chapters = info.get('chapters', []) if info else []


Summary: auto


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
        del waveform  # Free memory
    except Exception as e:
        logger.warning(f"Could not get audio info via torchaudio: {e}, trying ffprobe")
        try:
            # Fallback to ffprobe
            probe_cmd = [
                'ffprobe', '-v', 'error', '-show_entries',
                'format=duration:stream=sample_rate', '-of', 'json', str(raw_file)
            ]
            probe_result = subprocess.run(probe_cmd, capture_output=True, text=True)
            import json
            probe_data = json.loads(probe_result.stdout)
            actual_duration = float(probe_data.get('format', {}).get('duration', 0))
            original_sr = int(probe_data.get('streams', [{}])[0].get('sample_rate', 48000))
        except Exception as e2:
            logger.warning(f"ffprobe also failed: {e2}")
            original_sr = 48000
            actual_duration = 0

    logger.info(f"   📊 Raw audio: {original_sr}Hz, {actual_duration:.1f}s")

    # NOTE: Spectral analysis moved to AFTER intro skip (analyzes actual speech content)

    # === v3: Use Supabase metadata for chapter-based intro skip ===
    # Chapter info now comes from Supabase (synced from CSV), NOT yt-dlp
    intro_skip = 0.0
    outro_skip = 0.0
    skip_reason = "none"
    matched_title = None
    video_title = f'R2 Source: {video_id}'

    # Use Supabase metadata if provided
    if supabase_metadata:
        video_title = supabase_metadata.get('title') or video_title
        chapters = supabase_metadata.get('chapters') or []
        has_chapters = supabase_metadata.get('has_chapters', False)
        source_duration = supabase_metadata.get('source_duration_min', 0) * 60 if supabase_metadata.get('source_duration_min') else actual_duration

        if chapters and has_chapters:
            logger.info(f"   📖 Using Supabase chapters ({len(chapters)} chapters)")
            # Convert chapter format if needed (Supabase stores as JSON with 'seconds' key)
            normalized_chapters = []
            for i, ch in enumerate(chapters):
                start_sec = ch.get('seconds', ch.get('start_time', 0))
                # Calculate end_time from next chapter or total duration
                if i + 1 < len(chapters):
                    end_sec = chapters[i + 1].get('seconds', chapters[i + 1].get('start_time', source_duration))
                else:
                    end_sec = source_duration
                normalized_chapters.append({
                    'title': ch.get('title', ''),
                    'start_time': start_sec,
                    'end_time': end_sec,
                })

            # Use chapter-based intro detection
            intro_skip, outro_skip, skip_reason, matched_title = detect_intro_outro_from_chapters(
                normalized_chapters, source_duration, config
            )
            logger.info(f"   📖 Chapter-based skip: intro={intro_skip:.1f}s, reason={skip_reason}")
        else:
            # No chapters in Supabase, use dynamic skip
            logger.info(f"   📊 No chapters in Supabase, using dynamic skip")
            if actual_duration > 0:
                # FIX: Removed 10% cap that was overriding 3min/5min logic
                # The get_dynamic_intro_skip function already implements proper duration-based skip
                intro_skip = get_dynamic_intro_skip(actual_duration)
                skip_reason = "dynamic:no_supabase_chapters"
                logger.info(f"   ⏱ Dynamic skip: {intro_skip:.0f}s")
    else:
        # No Supabase metadata provided, use dynamic skip
        logger.info(f"   ⚠ No Supabase metadata, using dynamic skip")
        if actual_duration > 0 and getattr(config, 'auto_intro_skip', True) and getattr(config, 'intro_skip_seconds', 0.0) == 0.0:
            # NOTE: get_dynamic_intro_skip already implements the intended policy:
            # - ≤30min: 180s (3min)
            # - >30min: 300s (5min)
            # A previous 10% cap here caused regressions (e.g., ~52s for an 8.6min file).
            intro_skip = get_dynamic_intro_skip(actual_duration)
            skip_reason = "dynamic:no_metadata"
            logger.info(f"   ⏱ Dynamic skip: {intro_skip:.0f}s")


**Evaluating database setup**

I should probably set a default while ensuring `max_attempts=3` and `attempt_count=0`. There might be other required columns in the database, so inspecting `scripts/test_analytics.py` for column listings could help. The simplest move might be to attempt inserting a row to check for errors, but I need to avoid duplication. Perhaps querying the table schema through Postgres's information schema would provide better insight. The previous error suggests a possible issue with the table name; I believe it's `videos`.


**Computing video analytics**

Next, I'm computing analytics both pre and post. First, I’ll read the CSV to check the unique video IDs—it says 50k in the filename, but I only found 40,237. I need to query Supabase to find which IDs already exist and gather their statuses in chunks. I'll determine how many are already present and their statuses like COMPLETED, PENDING, CLAIMED, etc. Then, I can calculate how many new IDs there are and insert them with a 'pending' status. Finally, I’ll verify the counts and compute global metrics based on updated totals.


**Updating global metrics**

I'm planning to compute new global metrics by comparing the before and after data. The user wants analytics on existing video IDs in Supabase versus new ones added as pending. This includes the total from the CSV, the count currently in the database with a status breakdown, and the count of newly inserted pending videos. I'll ensure that failed entries in the CSV remain unchanged by using `ignore_duplicates`. Now, I need to implement and run the script, making sure not to expose any sensitive information like full URL keys in the output.


**Planning report generation**

I don't need to log counts beyond what's necessary, but I could create a report file for inserted IDs, like `reports/sync_telugu_20260112.json`, even though the user didn't request it. Keeping things minimal might be best. I could run the sync from Python using `-c` in the command line, but adding a script for reproducibility is likely a better choice. I should check out existing scripts, such as `scripts/find_valid_videos.py` and `scripts/check_videos.py`, to see if there's already one that syncs discovered podcasts. I'll search for 'podcast_probability' usage specifically.


rg -n "podcast_probability|music_contamination_risk|source_keyword|discovered_at|classification_label|channel_id" -S .


Output:
Exit code: 1
Wall time: 0.2 seconds
Output:


**Updating analytics in Supabase**

The user wants an updated report for Supabase analytics, which includes overall summary metrics like the total number of videos in the database. I’ll focus on updating only the changed values, such as the increased total count from newly inserted videos.

They might also want the Telugu count, especially in relation to existing video IDs versus new videos added to pending. However, I’ll ensure completed analytics doesn’t include pending videos since they don't contribute to usable metrics like audio hours. I’ll clarify updates for both total videos and completed counts.


**Computing video counts**

I need to state that some counts will remain unchanged, but I’ll still compute total videos, pending counts, and others. For the new report, the total equals old counts plus inserted videos, while pending equals old pending plus the inserted amount. I’ll implement a script to print analytics before and after changes, capturing the video status counts.

I might also check Telugu counts, ensuring that the language is set correctly. I’ll query the current Telugu counts before the additions and use SupabaseClient.get_stats for aggregated data, which simplifies the process.


**Implementing Supabase script**

I’m planning to implement a script using supabase-py because it simplifies the process. I’ll start by loading dotenv and creating a client with the admin URL. I’ll read a CSV into a DataFrame and validate video IDs with a regex to ensure they are unique.

For pre-stats, I'll get overall status counts, starting with total counts by using `.select('youtube_id', count='exact').limit(1)`, which gives me just the data I need. Then, I’ll check which IDs in the CSV already exist by querying for matching rows. Finally, I’ll compute the new IDs versus existing ones and prepare rows for insertion from the CSV.


**Upserting CSV data**

I’m thinking we can upsert all the CSV IDs while ignoring duplicates. However, since the payload might be really large (about 40k), I’ll batch the upsert to around 1000 at a time. If I include existing rows, it will ignore duplicates without updating, so it's simpler to just send all.

To reduce the load, I’ll send only the new rows while keeping in mind the possibility of conflicts from new entries being added in between. For the batch insertion, I’ll use `client.table('videos').upsert(batch, ignore_duplicates=True)` and potentially request a count to see how many were inserted. If that doesn’t work, I’ll simply re-query the IDs to check.


**Finalizing upsert logic**

I’m realizing that PostgREST's upsert with ignore duplicates might return 0, meaning I should avoid counting based on the response. After the insert, I’ll query the existing count among the CSV IDs to confirm how many were actually inserted.

I’ll compute the difference to find out how many were successful, but I don't need to update any statuses. Then I’ll recheck the global status counts and summarize the results.

I initially thought about providing a machine-readable report, but it might be too large, so I’ll skip that. I also need to ensure invalid video IDs aren’t inserted, and I’ll count them as failed. Since the CSV has around 40k unique entries, I’ll also be setting the status for inserted IDs as ‘PENDING.’ I’ll make sure I use uppercase for the status. Now it’s time to implement the script!


Output: Plan updated


**Creating a generic sync script**

I’m thinking about creating a script called `sync_discovered_csv_to_supabase.py` for better versatility, even though the user initially asked for a specific file for Telugu. I’ll set it up to take a CSV input with `argparse`, using the discover path as a default. I need some options like batch size, a dry-run flag, and potentially a way to set existing statuses to pending without altering failed ones. Maybe I’ll keep that as an optional flag too, since the user's request indicates they want all set to pending.


**Interpreting the user's request**

I need to carefully understand the user's request about adding new Telugu video IDs with their metadata and setting them to pending. They want to trigger AI clusters to process these videos but don’t mention requeueing existing IDs, so I think it's safe to keep that option off. The user also wants analytics comparing existing video IDs to the new ones added, indicating existing statuses shouldn’t change. I’ll ensure the script doesn’t update existing IDs, focusing only on the new ones.


**Outlining script steps**

I’m starting by loading the environment variables using dotenv. It looks like the URL is specified in the `.env` file, so I’ll create the client with that URL and key. I need to gather status counts using a helper function, but since they can be large, I'll utilize `SupabaseClient.get_stats` for efficiency.

To get counts for each status, I'll use `supabase-py`, which will help me fetch specific counts by status, and total counts as well as counts by language. Then, I’ll query existing CSV IDs in chunks and finally insert new rows with timestamps.


**Structuring data insertion steps**

For the `extra_meta`, I should ensure it's JSON serializable by converting pandas types like Timestamp and float to Python primitives, like using `row.get('podcast_probability')` for float values. All rows will have the language set to 'telugu', which I can derive from a mapping.

I’ll use `datetime.now(timezone.utc).isoformat()` for the `created_at` and `updated_at` fields, which should work since the DB expects timestamps with timezones.

If I set a dry run, I’ll just print a summary and exit. After inserting the data, I'll need to re-query to compute final counts. I think a quick dry run will help clarify what would happen before executing the real insertion.


**Planning and optimizing script execution**

I’m going to run the script with `--dry-run` first to ensure the logic is correct and catch any potential errors. This keeps things safe while we check for existing entries. With 40k records, processing checks in 200-chunks means about 201 calls, which is manageable, but I need to remember Supabase's rate limits.

Inserting will use an upsert of 500 per batch, leading to around 80 calls. This should take minutes, which is acceptable. I’ll also handle any API errors by tracking failed inserts per batch, and if a batch fails, I’ll split it into smaller chunks to debug. Additionally, I’ll adjust the chunk size if I run into URL length issues. Let’s implement the script!


Input:
*** Begin Patch
*** Add File: scripts/sync_discovered_csv_to_supabase.py
+#!/usr/bin/env python3
+"""
+Sync a "discovered videos" CSV into Supabase `videos` table.
+
+This script is designed for incremental ingestion:
+- Inserts missing youtube_ids as `PENDING`
+- Does NOT modify existing rows (FAILED stays FAILED, COMPLETED stays COMPLETED, etc.)
+
+CSV expected columns (minimum):
+  - video_id
+
+Optional columns stored into Supabase columns / extra_meta:
+  - title, duration_seconds, channel_id, source_lang, podcast_probability,
+    music_contamination_risk, source_keyword, discovered_at
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import sys
+from dataclasses import dataclass
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Optional, Set, Tuple
+
+import pandas as pd
+from dotenv import load_dotenv
+
+
+STATUSES = ["PENDING", "CLAIMED", "COMPLETED", "FAILED", "SKIPPED"]
+
+
+def _now_iso() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _chunked(items: List[str], size: int) -> Iterable[List[str]]:
+    for i in range(0, len(items), size):
+        yield items[i : i + size]
+
+
+def _is_valid_youtube_id(video_id: str) -> bool:
+    # YouTube IDs are typically 11 chars of [A-Za-z0-9_-]
+    if not isinstance(video_id, str):
+        return False
+    if len(video_id) != 11:
+        return False
+    for ch in video_id:
+        if not (ch.isalnum() or ch in "_-"):
+            return False
+    return True
+
+
+def _language_from_source_lang(source_lang: Optional[str]) -> Optional[str]:
+    if not source_lang:
+        return None
+    mapping = {
+        "te": "telugu",
+        "ta": "tamil",
+        "hi": "hi",
+        "ml": "malayalam",
+        "kn": "kannada",
+        "bn": "bengali",
+        "gu": "gujarati",
+        "pa": "punjabi",
+        "or": "odia",
+    }
+    return mapping.get(str(source_lang).strip().lower(), str(source_lang).strip().lower())
+
+
+def _jsonable(value: Any) -> Any:
+    if value is None:
+        return None
+    if isinstance(value, (str, int, float, bool)):
+        return value
+    if isinstance(value, (datetime,)):
+        return value.isoformat()
+    # pandas/numpy scalars
+    try:
+        if pd.isna(value):
+            return None
+    except Exception:
+        pass
+    try:
+        if hasattr(value, "item"):
+            return value.item()
+    except Exception:
+        pass
+    return str(value)
+
+
+@dataclass(frozen=True)
+class SyncAnalytics:
+    csv_rows: int
+    csv_unique_ids: int
+    csv_invalid_ids: int
+    supabase_existing_ids: int
+    supabase_existing_by_status: Dict[str, int]
+    inserted_new_ids: int
+    inserted_failed_ids: int
+
+
+def _get_supabase_client():
+    try:
+        from supabase import create_client  # type: ignore
+    except Exception as e:  # pragma: no cover
+        raise RuntimeError("Missing dependency: `supabase` (pip install supabase)") from e
+
+    url = os.environ.get("URL") or os.environ.get("SUPABASE_URL") or ""
+    key = os.environ.get("SUPABASE_ADMIN") or ""
+    if not url or not key:
+        raise RuntimeError("Missing Supabase credentials: set `URL` and `SUPABASE_ADMIN` (or via .env).")
+    return create_client(url, key)
+
+
+def _count_videos_by_status(sb) -> Dict[str, int]:
+    counts: Dict[str, int] = {}
+    for status in STATUSES:
+        res = sb.table("videos").select("youtube_id", count="exact").eq("status", status).limit(1).execute()
+        counts[status] = int(getattr(res, "count", 0) or 0)
+    return counts
+
+
+def _count_total_videos(sb) -> int:
+    res = sb.table("videos").select("youtube_id", count="exact").limit(1).execute()
+    return int(getattr(res, "count", 0) or 0)
+
+
+def _fetch_existing_statuses(
+    sb,
+    youtube_ids: List[str],
+    chunk_size: int,
+) -> Dict[str, str]:
+    existing: Dict[str, str] = {}
+    for batch in _chunked(youtube_ids, chunk_size):
+        rows = sb.table("videos").select("youtube_id,status").in_("youtube_id", batch).execute().data or []
+        for r in rows:
+            yid = r.get("youtube_id")
+            if not yid:
+                continue
+            existing[str(yid)] = str(r.get("status") or "")
+    return existing
+
+
+def _build_rows_to_insert(df: pd.DataFrame, youtube_ids: Set[str]) -> List[Dict[str, Any]]:
+    now = _now_iso()
+    out: List[Dict[str, Any]] = []
+
+    for _, row in df.iterrows():
+        video_id = str(row["video_id"])
+        if video_id not in youtube_ids:
+            continue
+
+        duration_seconds = row.get("duration_seconds")
+        duration_min = None
+        try:
+            if duration_seconds is not None and not pd.isna(duration_seconds):
+                duration_min = float(duration_seconds) / 60.0
+        except Exception:
+            duration_min = None
+
+        source_lang = row.get("source_lang")
+        language = _language_from_source_lang(_jsonable(source_lang))
+
+        extra_meta = {
+            "source_lang": _jsonable(source_lang),
+            "channel_id": _jsonable(row.get("channel_id")),
+            "podcast_probability": _jsonable(row.get("podcast_probability")),
+            "music_contamination_risk": _jsonable(row.get("music_contamination_risk")),
+            "source_keyword": _jsonable(row.get("source_keyword")),
+            "discovered_at": _jsonable(row.get("discovered_at")),
+            "source_csv": "discovered",
+        }
+
+        out.append(
+            {
+                "youtube_id": video_id,
+                "youtube_url": f"https://www.youtube.com/watch?v={video_id}",
+                "status": "PENDING",
+                "created_at": now,
+                "updated_at": now,
+                "title": _jsonable(row.get("title")),
+                "source_duration_min": duration_min,
+                "language": language,
+                # We only have channel_id here; store it in `channel` for worker visibility.
+                "channel": _jsonable(row.get("channel_id")),
+                "max_attempts": 3,
+                "attempt_count": 0,
+                "extra_meta": extra_meta,
+            }
+        )
+
+    return out
+
+
+def _safe_upsert_ignore_duplicates(sb, rows: List[Dict[str, Any]], batch_size: int) -> Tuple[int, List[Tuple[str, str]]]:
+    """
+    Upsert with ignore_duplicates=True so existing rows are NOT modified.
+
+    Returns: (attempted_rows, failures [(youtube_id, error)])
+    """
+
+    failures: List[Tuple[str, str]] = []
+
+    def send(batch: List[Dict[str, Any]]) -> None:
+        if not batch:
+            return
+        sb.table("videos").upsert(
+            batch,
+            on_conflict="youtube_id",
+            ignore_duplicates=True,
+            returning="minimal",
+        ).execute()
+
+    def bisect_and_send(batch: List[Dict[str, Any]]) -> None:
+        if not batch:
+            return
+        try:
+            send(batch)
+        except Exception as e:
+            if len(batch) == 1:
+                failures.append((str(batch[0].get("youtube_id") or ""), str(e)))
+                return
+            mid = len(batch) // 2
+            bisect_and_send(batch[:mid])
+            bisect_and_send(batch[mid:])
+
+    for batch in _chunked(rows, batch_size):
+        bisect_and_send(batch)
+
+    return len(rows), failures
+
+
+def sync(csv_path: Path, batch_size: int, lookup_chunk_size: int, dry_run: bool) -> Tuple[SyncAnalytics, Dict[str, int], Dict[str, int]]:
+    sb = _get_supabase_client()
+
+    pre_total = _count_total_videos(sb)
+    pre_by_status = _count_videos_by_status(sb)
+
+    df = pd.read_csv(csv_path)
+    if "video_id" not in df.columns:
+        raise RuntimeError(f"CSV missing required column `video_id`: {csv_path}")
+
+    raw_ids = [str(v) for v in df["video_id"].tolist()]
+    unique_ids: List[str] = list(dict.fromkeys(raw_ids))
+
+    invalid_ids = [v for v in unique_ids if not _is_valid_youtube_id(v)]
+    valid_ids = [v for v in unique_ids if v not in set(invalid_ids)]
+
+    existing = _fetch_existing_statuses(sb, valid_ids, chunk_size=lookup_chunk_size)
+    existing_ids = set(existing.keys())
+    existing_by_status: Dict[str, int] = {}
+    for s in STATUSES:
+        existing_by_status[s] = 0
+    for st in existing.values():
+        existing_by_status[st] = existing_by_status.get(st, 0) + 1
+
+    to_insert_ids = set(valid_ids) - existing_ids
+    rows_to_insert = _build_rows_to_insert(df, to_insert_ids)
+
+    attempted = 0
+    failures: List[Tuple[str, str]] = []
+    if dry_run:
+        attempted = len(rows_to_insert)
+    else:
+        attempted, failures = _safe_upsert_ignore_duplicates(sb, rows_to_insert, batch_size=batch_size)
+
+    # Verify how many now exist (including those inserted successfully).
+    existing_after = _fetch_existing_statuses(sb, valid_ids, chunk_size=lookup_chunk_size)
+    inserted_success = len(existing_after) - len(existing_ids)
+
+    post_total = _count_total_videos(sb)
+    post_by_status = _count_videos_by_status(sb)
+
+    analytics = SyncAnalytics(
+        csv_rows=int(len(df)),
+        csv_unique_ids=int(len(unique_ids)),
+        csv_invalid_ids=int(len(invalid_ids)),
+        supabase_existing_ids=int(len(existing_ids)),
+        supabase_existing_by_status=existing_by_status,
+        inserted_new_ids=int(inserted_success),
+        inserted_failed_ids=int(len(set(yid for yid, _ in failures if yid))),
+    )
+
+    return analytics, {"total": pre_total, **pre_by_status}, {"total": post_total, **post_by_status}
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Sync discovered videos CSV into Supabase `videos`.")
+    parser.add_argument(
+        "--csv",
+        type=Path,
+        default=Path("discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv"),
+        help="Path to discovered CSV file",
+    )
+    parser.add_argument("--batch-size", type=int, default=500, help="Upsert batch size")
+    parser.add_argument("--lookup-chunk-size", type=int, default=200, help="Chunk size for existing-id lookups")
+    parser.add_argument("--dry-run", action="store_true", help="Compute analytics without writing to Supabase")
+    args = parser.parse_args()
+
+    load_dotenv()
+
+    if not args.csv.exists():
+        print(f"ERROR: CSV not found: {args.csv}", file=sys.stderr)
+        return 2
+
+    analytics, pre_counts, post_counts = sync(
+        csv_path=args.csv,
+        batch_size=args.batch_size,
+        lookup_chunk_size=args.lookup_chunk_size,
+        dry_run=args.dry_run,
+    )
+
+    print("=== DISCOVERED CSV → SUPABASE SYNC ===")
+    print(f"CSV: {args.csv}")
+    print(f"CSV rows: {analytics.csv_rows}")
+    print(f"Unique video_ids: {analytics.csv_unique_ids}")
+    print(f"Invalid video_ids skipped: {analytics.csv_invalid_ids}")
+    print("")
+    print("Among CSV IDs (pre-sync):")
+    print(f"  Already in Supabase: {analytics.supabase_existing_ids}")
+    for st in STATUSES:
+        if analytics.supabase_existing_by_status.get(st, 0):
+            print(f"    - {st}: {analytics.supabase_existing_by_status.get(st, 0)}")
+    print(f"  New IDs to insert as PENDING: {analytics.csv_unique_ids - analytics.csv_invalid_ids - analytics.supabase_existing_ids}")
+    if args.dry_run:
+        print("")
+        print("[DRY RUN] No changes written.")
+    else:
+        print("")
+        print("Write results:")
+        print(f"  Inserted new IDs: {analytics.inserted_new_ids}")
+        print(f"  Failed inserts: {analytics.inserted_failed_ids}")
+
+    print("")
+    print("Supabase `videos` status counts (pre → post):")
+    for key in ["total", *STATUSES]:
+        pre = pre_counts.get(key, 0)
+        post = post_counts.get(key, 0)
+        if pre != post:
+            print(f"  {key}: {pre} → {post} (Δ {post - pre:+})")
+        else:
+            print(f"  {key}: {pre} → {post}")
+
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())
+
*** End Patch


Output: {"output":"Success. Updated the following files:\nA scripts/sync_discovered_csv_to_supabase.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


Output:
Exit code: 0
Wall time: 55.7 seconds
Output:
=== DISCOVERED CSV → SUPABASE SYNC ===
CSV: discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv
CSV rows: 40237
Unique video_ids: 40237
Invalid video_ids skipped: 0

Among CSV IDs (pre-sync):
  Already in Supabase: 0
  New IDs to insert as PENDING: 40237

[DRY RUN] No changes written.

Supabase `videos` status counts (pre → post):
  total: 487830 → 487830
  PENDING: 28 → 28
  CLAIMED: 307 → 307
  COMPLETED: 471431 → 471431
  FAILED: 16064 → 16064
  SKIPPED: 0 → 0


**Preparing data sync**

I found that none exist in Supabase, so it's safe to insert all 40,237 rows. The pre-counts show 28 pending, 307 claimed, 471,431 completed, and 16,064 failed, totaling 487,830. These numbers align with the given report. Now, I need to sync and set new ones to pending. I’ll run the script without a dry run, expecting to create about 80 upsert batches, which might take a few minutes, around 10 minutes.


Output:
Exit code: 1
Wall time: 110.9 seconds
Output:
Traceback (most recent call last):
  File "/home/ubuntu/maya3/scripts/sync_discovered_csv_to_supabase.py", line 351, in <module>
    raise SystemExit(main())
  File "/home/ubuntu/maya3/scripts/sync_discovered_csv_to_supabase.py", line 309, in main
    analytics, pre_counts, post_counts = sync(
  File "/home/ubuntu/maya3/scripts/sync_discovered_csv_to_supabase.py", line 274, in sync
    post_total = _count_total_videos(sb)
  File "/home/ubuntu/maya3/scripts/sync_discovered_csv_to_supabase.py", line 127, in _count_total_videos
    res = sb.table("videos").select("youtube_id", count="exact").limit(1).execute()
  File "/home/ubuntu/.local/lib/python3.10/site-packages/postgrest/_sync/request_builder.py", line 53, in execute
    raise APIError(dict(json_obj))
postgrest.exceptions.APIError: {'message': 'canceling statement due to statement timeout', 'code': '57014', 'hint': None, 'details': None}


Summary: auto


**Troubleshooting Postgres counts**

It seems like the insertion may have worked partially, but the script crashed while trying to count due to a statement timeout. The count timing out could be because the table is heavy now. Previously, it succeeded, so maybe the count queries are slower now or the timeout got lowered? I need to find an alternative method for counting, such as using `SupabaseClient.get_stats`. However, I must be cautious as it might still time out. Instead, I could calculate the total from the sum of status counts to avoid the full table count, which might be more efficient.


**Computing video counts**

I’m considering skipping the exact total count and computing the total as the sum of statuses instead. However, I need to ensure that statuses like "SKIPPED" cover everything. If there are unknown statuses, should I count those too? I think it’s safe to assume only known statuses will apply. So, I’ll patch the script to avoid using `count='exact'` without filters. Using known status counts for reporting seems more efficient.

The old total was 487,830, and it should be 528,067 after new inserts. I'll need to rerun the script and make it idempotent to manage any existing records. Also, I must adjust it to handle timeouts before running it again.


**Verifying insertion success**

I need to check if the insertion actually happened before the script crashed. It crashed during the post count after performing the upsert and verifying existing records. The sync code first does the upsert, fetches existing records, and then computes whether the insertion was successful before trying to count everything. Since the error occurred at `_count_total_videos`, it suggests the upsert worked.

I can rerun the script with `--dry-run` to evaluate the current state without making changes, but I need to patch the script first to avoid count errors. However, the dry-run might still trigger the total count, which succeeded before but could time out now. I need to be careful!


**Adjusting count methods**

I’m considering running the script with `--dry-run`, but I’m concerned that the total count might timeout given the larger database. The `pre_total` count might actually timeout now due to the increased table size, especially if the statement timeout is low. Instead of attempting the total exact count, I plan to avoid that altogether and patch the script to remove the `_count_total_videos` function.

I’ll compute the total from the sum of status counts, which should work since the `_count_videos_by_status` function uses indexing. If there are other statuses, they might be missed, but I believe the known statuses are sufficient. Additionally, I could try using `SupabaseClient.get_stats` with the Content-Range header for counts. I'll make sure we stick to supabase-py for this.


**Optimizing count logic**

I’m thinking it’s best to avoid counting before and after the insert across the entire table to prevent statement timeouts. We used `_fetch_existing_statuses` to verify the insertion, which is fine since it uses an `in_` filter. However, the error may stem from the statement timeout on Supabase, which is set to 60 seconds. Given the table now has 528k rows, counting exactly may take too long. To be safe, I’ll make the necessary patches to the script.


Input:
*** Begin Patch
*** Update File: scripts/sync_discovered_csv_to_supabase.py
@@
 def _count_videos_by_status(sb) -> Dict[str, int]:
     counts: Dict[str, int] = {}
     for status in STATUSES:
         res = sb.table("videos").select("youtube_id", count="exact").eq("status", status).limit(1).execute()
         counts[status] = int(getattr(res, "count", 0) or 0)
     return counts

-def _count_total_videos(sb) -> int:
-    res = sb.table("videos").select("youtube_id", count="exact").limit(1).execute()
-    return int(getattr(res, "count", 0) or 0)
+def _total_from_status_counts(by_status: Dict[str, int]) -> int:
+    return int(sum(int(by_status.get(s, 0) or 0) for s in STATUSES))
@@
 def sync(csv_path: Path, batch_size: int, lookup_chunk_size: int, dry_run: bool) -> Tuple[SyncAnalytics, Dict[str, int], Dict[str, int]]:
     sb = _get_supabase_client()

-    pre_total = _count_total_videos(sb)
     pre_by_status = _count_videos_by_status(sb)
+    pre_total = _total_from_status_counts(pre_by_status)
@@
-    post_total = _count_total_videos(sb)
     post_by_status = _count_videos_by_status(sb)
+    post_total = _total_from_status_counts(post_by_status)
*** End Patch


Output: {"output":"Success. Updated the following files:\nM scripts/sync_discovered_csv_to_supabase.py\n","metadata":{"exit_code":0,"duration_seconds":0.0}}


**Running script analysis**

I'm preparing to rerun the script. Since I've inserted some items already, it will detect existing IDs and only insert the remaining ones. However, checking the existing count might take a bit of time. I think it’s a good idea to first run a `--dry-run` to see the current existing count and what's still left to insert. That seems like a smart way to minimize any issues!


Output:
Exit code: 0
Wall time: 77.7 seconds
Output:
=== DISCOVERED CSV → SUPABASE SYNC ===
CSV: discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv
CSV rows: 40237
Unique video_ids: 40237
Invalid video_ids skipped: 0

Among CSV IDs (pre-sync):
  Already in Supabase: 40237
    - PENDING: 40237
  New IDs to insert as PENDING: 0

[DRY RUN] No changes written.

Supabase `videos` status counts (pre → post):
  total: 528067 → 528067
  PENDING: 40265 → 40265
  CLAIMED: 307 → 307
  COMPLETED: 471431 → 471431
  FAILED: 16064 → 16064
  SKIPPED: 0 → 0


**Calculating post-add analytics**

The dry-run indicates that all 40,237 items are already in Supabase and have been successfully inserted. Now, we need to compute the post-addition analytics that the user requested. I need to run a script to show values like total counts. With the previous total of 487,830 and pending of 28, the new total is 528,067 with pending at 40,265, giving a delta of +40,237. It’s important to highlight that all 40,237 are now pending.


**Setting IDs to pending**

I’ve set all IDs to pending after insertion, resulting in a pending count of 40,265. This indicates there were previously 28 pending, so now it’s 28 + 40,237. Next, I need to prepare the overall analytics report the user requested, focusing on key metrics. I will update table metrics, including total videos, completed, pending, failed, and claimed. Although pending doesn't impact usable hours, the user might want an updated total video count. I'll include those details too, as well as queue status and new inserted counts.


**Preparing analytics for video IDs**

The user has asked for analytics comparing existing video IDs in Supabase with the new videos added to pending. I need to provide details on 40,237 rows from the CSV, with 0 previously existing and all 40,237 inserted. I should also include pre and post queue status counts. They mentioned a Telugu count, which I can compute by querying the language set to 'telugu' and status pending. It might be a bit resource-intensive, but I can perform the query using the index on language. I’ll run a snippet to count pending Telugu videos in the database.


python3 - <<'PY'
import os
from dotenv import load_dotenv
from supabase import create_client
load_dotenv()
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])

def count(**filters):
    q=sb.table('videos').select('youtube_id', count='exact').limit(1)
    for k,v in filters.items():
        q=q.eq(k,v)
    res=q.execute()
    return int(getattr(res,'count',0) or 0)

print('pending_telugu', count(status='PENDING', language='telugu'))
print('total_telugu', count(language='telugu'))
PY


Output:
Exit code: 1
Wall time: 0.7 seconds
Output:
Traceback (most recent call last):
  File "<stdin>", line 4, in <module>
  File "/home/ubuntu/.local/lib/python3.10/site-packages/dotenv/main.py", line 346, in load_dotenv
    dotenv_path = find_dotenv()
  File "/home/ubuntu/.local/lib/python3.10/site-packages/dotenv/main.py", line 305, in find_dotenv
    assert frame.f_back is not None
AssertionError


**Troubleshooting dotenv loading**

It seems like I'm running into an issue with loading the .env file in Python because `find_dotenv` can’t access it due to standard input. I’ll need to pass the `dotenv_path` explicitly as `.env`. So, my plan is to use `load_dotenv(dotenv_path='.env')` and then rerun the code. Hopefully, that'll resolve the issue! Let’s see how it goes!


python3 - <<'PY'
import os
from dotenv import load_dotenv
from supabase import create_client
load_dotenv(dotenv_path='.env')
sb=create_client(os.environ['URL'], os.environ['SUPABASE_ADMIN'])

def count(**filters):
    q=sb.table('videos').select('youtube_id', count='exact').limit(1)
    for k,v in filters.items():
        q=q.eq(k,v)
    res=q.execute()
    return int(getattr(res,'count',0) or 0)

print('pending_telugu', count(status='PENDING', language='telugu'))
print('total_telugu', count(language='telugu'))
PY


Output:
Exit code: 0
Wall time: 10.7 seconds
Output:
pending_telugu 40237
total_telugu 63507


Output: Plan updated


**Computing post-insertion analytics**

I'm analyzing the addition analytics after the insertion process. From the dry run, there were counts for total, pending, claimed, completed, failed, and skipped entries. Now, after the update, I see increases, which is great! The delta shows a significant increase in total and pending counts. Additionally, there are no changes in claims or failures. I can provide a summary for the user that includes the new total videos and their unaltered completion status. Now, I’ll finalize this run and mark the steps accordingly.


Output: Plan updated


**Updating plan details**

I should note that the script can be rerun idempotently, meaning duplicates will be ignored. It seems important to reference the script file path, perhaps like `scripts/sync_discovered_csv_to_supabase.py`. However, I’m not entirely sure about the exact line for output, so I’ll just mention the path. I also need to update the plan status to indicate that step 4 is complete. I’ll move on to calling the update_plan now.


Output: Plan updated


- Synced `discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv` into Supabase `videos` (script: `scripts/sync_discovered_csv_to_supabase.py:1`) by inserting missing `youtube_id`s as `PENDING` without touching existing rows (so `FAILED` stays `FAILED`, etc.).
- CSV stats: `40,237` rows / `40,237` unique `video_id`s / `0` invalid IDs.
- Supabase overlap (pre-sync): `0` already existed in DB; `40,237` were new.
- Insert results: `40,237` new rows added and set to `PENDING` (failed inserts: `0`).
- Queue counts (pre → post):
  - `TOTAL`: `487,830 → 528,067` (Δ `+40,237`)
  - `PENDING`: `28 → 40,265` (Δ `+40,237`)
  - `CLAIMED`: `307 → 307`
  - `COMPLETED`: `471,431 → 471,431`
  - `FAILED`: `16,064 → 16,064`
- Telugu pending now in DB: `40,237` (these newly added).

If this looks correct, tell me to proceed and I’ll trigger the Vast.ai workers to start draining the new `PENDING` queue.


# Context from my IDE setup:

## Open tabs:
- 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt: 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt
- .env: .env
- discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv: discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv

## My request for Codex:
yes. time to trigger the workers. preference is 8*3090s or 8*4090s with highest internet speeds atleast 800mbps and more. target is for 10-15 clusters. but let's add them periodically. let's first deploy to one, i'll confirm and then we will keep adding few more together


Summary: auto


**Searching for GPU offers**

I’m planning to search for offers on both the 3090 and 4090 GPUs. I’ll set parameters like a minimum download speed of 800 and possibly allow for a minimum upload of 100, aiming for a minimum reliability of 0.90 and at least 400 for disk space. I might use parallel searches for both GPUs. I’ll also check and address an earlier error in the `.env` file. Let’s get started!


Output: Plan updated


python3 scripts/vast/search_and_deploy.py --help | sed -n '1,160p'


sed -n '1,120p' scripts/vast/search_and_deploy.py


python3 scripts/vast/vastctl.py --help | sed -n '1,120p'


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
usage: search_and_deploy.py [-h] [--api-key API_KEY] [--env-file ENV_FILE]
                            [--search] [--deploy] [--gpu-types GPU_TYPES]
                            [--num-gpus NUM_GPUS]
                            [--min-inet-down MIN_INET_DOWN]
                            [--min-inet-up MIN_INET_UP] [--min-disk MIN_DISK]
                            [--min-reliability MIN_RELIABILITY]
                            [--max-dph MAX_DPH]
                            [--deny-countries DENY_COUNTRIES] [--count COUNT]
                            [--image IMAGE] [--disk DISK]
                            [--label-prefix LABEL_PREFIX]
                            [--r2-prefix R2_PREFIX] [--dry-run]

Search and deploy Maya3 to Vast.ai

options:
  -h, --help            show this help message and exit
  --api-key API_KEY     Vast API key
  --env-file ENV_FILE   Path to .env file
  --search              Search for offers (don't deploy)
  --deploy              Search and deploy
  --gpu-types GPU_TYPES
                        GPU types (comma-sep)
  --num-gpus NUM_GPUS   Number of GPUs per machine
  --min-inet-down MIN_INET_DOWN
                        Min download Mbps
  --min-inet-up MIN_INET_UP
                        Min upload Mbps
  --min-disk MIN_DISK   Min available disk GB
  --min-reliability MIN_RELIABILITY
                        Min reliability score
  --max-dph MAX_DPH     Max $/hour
  --deny-countries DENY_COUNTRIES
                        Blocked country codes
  --count COUNT         Number of instances to deploy
  --image IMAGE
  --disk DISK           Disk space to allocate GB
  --label-prefix LABEL_PREFIX
  --r2-prefix R2_PREFIX
                        R2 upload prefix
  --dry-run             Don't actually deploy


Output:
Exit code: 0
Wall time: 0 seconds
Output:
#!/usr/bin/env python3
"""
Search and deploy Maya3 workers to Vast.ai automatically.

Searches for offers matching criteria (GPU type, count, internet speed, region)
and deploys instances.

Usage:
  # Search for 8x3090 or 8x4090 offers
  python scripts/vast/search_and_deploy.py --search --gpu-types RTX_3090,RTX_4090 --num-gpus 8

  # Deploy to first matching offer
  python scripts/vast/search_and_deploy.py --deploy --gpu-types RTX_3090,RTX_4090 --num-gpus 8 --count 1

  # Dry run (search without deploying)
  python scripts/vast/search_and_deploy.py --deploy --dry-run --gpu-types RTX_3090,RTX_4090 --num-gpus 8

Env:
  - VAST_API_KEY or VAST_AI
  - .env for Maya3 settings
"""

from __future__ import annotations

import argparse
import json
import os
import sys
import time
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Sequence, Tuple
from urllib.error import HTTPError, URLError
from urllib.parse import urlencode
from urllib.request import Request, urlopen

BASE_URL = "https://console.vast.ai/api/v0"

# GPU name mappings (Vast uses various formats)
GPU_ALIASES = {
    "RTX_3090": ["RTX 3090", "GeForce RTX 3090", "NVIDIA GeForce RTX 3090"],
    "RTX_4090": ["RTX 4090", "GeForce RTX 4090", "NVIDIA GeForce RTX 4090"],
    "RTX_4080": ["RTX 4080", "GeForce RTX 4080", "NVIDIA GeForce RTX 4080"],
    "RTX_A6000": ["RTX A6000", "NVIDIA RTX A6000"],
    "A100": ["A100", "NVIDIA A100", "A100-SXM4-40GB", "A100-SXM4-80GB", "A100-PCIE-40GB", "A100-PCIE-80GB"],
    "H100": ["H100", "NVIDIA H100"],
}

# Slow/blocked regions
BLOCKED_COUNTRIES = {"CN", "RU", "IR", "KP", "BY"}

def _load_dotenv(path: str) -> None:
    try:
        from dotenv import load_dotenv
        load_dotenv(dotenv_path=path, override=False)
    except Exception:
        pass

def _get_api_key(explicit: Optional[str] = None) -> str:
    key = explicit or os.environ.get("VAST_API_KEY") or os.environ.get("VAST_AI") or ""
    if not key:
        raise SystemExit("Missing Vast API key. Set VAST_API_KEY or VAST_AI.")
    return key

def _request_json(
    api_key: str,
    method: str,
    path: str,
    *,
    body: Optional[Dict[str, Any]] = None,
    params: Optional[Dict[str, Any]] = None,
    timeout_sec: int = 60,
) -> Any:
    url = f"{BASE_URL}{path}"
    if params:
        url = f"{url}?{urlencode(params)}"

    data = None
    headers = {"Authorization": f"Bearer {api_key}"}
    if body is not None:
        data = json.dumps(body).encode("utf-8")
        headers["Content-Type"] = "application/json"

    req = Request(url, data=data, headers=headers, method=method)
    try:
        with urlopen(req, timeout=timeout_sec) as resp:
            raw = resp.read()
        return json.loads(raw.decode("utf-8")) if raw else None
    except HTTPError as e:
        err = e.read().decode("utf-8", errors="replace")
        raise RuntimeError(f"Vast API HTTP {e.code} {method} {path}: {err}") from e
    except URLError as e:
        raise RuntimeError(f"Vast API network error: {e}") from e

def _country_code(geolocation: str) -> str:
    """Extract country code from geolocation string."""
    parts = [p.strip() for p in (geolocation or "").split(",") if p.strip()]
    if not parts:
        return ""
    last = parts[-1]
    if len(last) == 2 and last.isalpha():
        return last.upper()
    return ""

@dataclass
class Offer:
    id: int
    gpu_name: str
    num_gpus: int
    dph_total: float  # $/hour total
    inet_down: float  # Mbps
    inet_up: float
    disk_space: float  # GB available
    reliability: float
    geolocation: str
    verification: str


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
usage: vastctl.py [-h] [--api-key API_KEY]
                  {instances,destroy,recycle,logs} ...

Vast.ai helper

positional arguments:
  {instances,destroy,recycle,logs}
    instances           List instances
    destroy             Destroy instance(s)
    recycle             Recycle instance(s)
    logs                Fetch instance logs

options:
  -h, --help            show this help message and exit
  --api-key API_KEY     Vast API key (or set VAST_API_KEY)


python3 scripts/vast/search_and_deploy.py --search --gpu-types RTX_3090,RTX_4090 --num-gpus 8 --min-inet-down 800 --min-inet-up 100 --min-disk 400 --min-reliability 0.90 | sed -n '1,260p'


Output:
Exit code: 0
Wall time: 0.4 seconds
Output:
Searching for 8x GPUs (['RTX_3090', 'RTX_4090'])...
  Min download: 800.0 Mbps
  Min upload: 100.0 Mbps
  Min disk: 400.0 GB
  Min reliability: 0.9
  Blocked countries: {'IR', 'BY', 'RU', 'CN', 'KP'}
  Found 12 raw offers

================================================================================
Found 12 matching offers:
================================================================================
  1. Offer 28498167: 8x RTX 3090 | $1.71/hr | 912/871 Mbps | 4586GB | rel=0.999 | US
  2. Offer 29888966: 8x RTX 4090 | $1.92/hr | 1090/1601 Mbps | 7097GB | rel=0.974 | NL
  3. Offer 28731612: 8x RTX 4090 | $2.14/hr | 5112/4042 Mbps | 3661GB | rel=0.995 | NL
  4. Offer 29347820: 8x RTX 4090 | $2.35/hr | 891/625 Mbps | 2581GB | rel=0.998 | TW
  5. Offer 29796108: 8x RTX 4090 | $2.56/hr | 1666/927 Mbps | 6437GB | rel=0.998 | TW
  6. Offer 27444543: 8x RTX 4090 | $2.75/hr | 938/516 Mbps | 3136GB | rel=0.978 | TH
  7. Offer 28881648: 8x RTX 4090 | $2.88/hr | 7142/5779 Mbps | 1937GB | rel=0.988 | GB
  8. Offer 29914560: 8x RTX 4090 | $2.88/hr | 7278/5430 Mbps | 2557GB | rel=0.968 | GB
  9. Offer 28017405: 8x RTX 4090 | $3.20/hr | 4356/4907 Mbps | 3227GB | rel=0.977 | NO
  10. Offer 26489207: 8x RTX 4090 | $3.20/hr | 4156/4869 Mbps | 4501GB | rel=0.998 | NO
  11. Offer 28691926: 8x RTX 4090 | $3.20/hr | 4285/2581 Mbps | 2739GB | rel=0.994 | NO
  12. Offer 28017356: 8x RTX 4090 | $3.20/hr | 890/914 Mbps | 3039GB | rel=0.998 | NO


**Evaluating offer selection criteria**

I’m considering the best way to select offers based on speed. It seems the script might allow selecting by offer ID, but that’s not in the help. I can adjust criteria to focus on offers with a minimum of 7000 for download and 5000 for upload. This way, I should isolate two offers.

To ensure I choose the highest speed one, I’ll set my download minimum to 7200. I’m keeping an eye on the offers and their sorting, especially regarding the blocked countries list.


rg -n "matching offers|sort\(|sorted\(|Offer\(" scripts/vast/search_and_deploy.py | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
198:            offer = Offer(
232:    offers.sort(key=lambda o: o.dph_total)
364:    print(f"Found {len(offers)} matching offers:")


sed -n '210,270p' scripts/vast/search_and_deploy.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
            )
        except Exception:
            continue

        # Filter by country
        cc = _country_code(offer.geolocation)
        if cc and cc in deny_countries:
            continue

        # Re-check criteria (API filtering isn't always exact)
        if offer.inet_down < min_inet_down:
            continue
        if offer.inet_up < min_inet_up:
            continue
        if offer.disk_space < min_disk:
            continue
        if offer.reliability < min_reliability:
            continue

        offers.append(offer)

    # Sort by price
    offers.sort(key=lambda o: o.dph_total)

    return offers[:limit]

def _build_env_map(r2_prefix: str = "") -> Dict[str, str]:
    """Build environment variable map for Vast instance."""
    required = [
        "URL",
        "SUPABASE_ADMIN",
        "HF_TOKEN",
        "R2_ENDPOINT_URL",
        "R2_ACCESS_KEY_ID",
        "R2_SECRET_ACCESS_KEY",
    ]
    missing = [k for k in required if not os.environ.get(k)]
    if missing:
        raise SystemExit(f"Missing required env vars: {', '.join(missing)}")

    env_map: Dict[str, str] = {
        "-e URL": os.environ["URL"],
        "-e SUPABASE_ADMIN": os.environ["SUPABASE_ADMIN"],
        "-e HF_TOKEN": os.environ["HF_TOKEN"],
        "-e R2_ENDPOINT_URL": os.environ["R2_ENDPOINT_URL"],
        "-e R2_ACCESS_KEY_ID": os.environ["R2_ACCESS_KEY_ID"],
        "-e R2_SECRET_ACCESS_KEY": os.environ["R2_SECRET_ACCESS_KEY"],
        "-e WORKER_PROVIDER": os.environ.get("WORKER_PROVIDER", "vast"),
        "-e SUPABASE_HTTP_TIMEOUT_SEC": os.environ.get("SUPABASE_HTTP_TIMEOUT_SEC", "60"),
        "-e SUPABASE_HTTP_MAX_ATTEMPTS": os.environ.get("SUPABASE_HTTP_MAX_ATTEMPTS", "6"),
        "-e R2_PREFIX": r2_prefix,
    }
    return env_map

def deploy_offer(
    api_key: str,
    offer: Offer,
    *,
    image: str,


**Evaluating deployment options**

I’m sorting the offers by price, starting with the cheapest that meet the constraints. By isolating to one specific offer, I see that setting minimum download to 7200 and upload to 5000 gives me one viable option. However, I’m considering other offers too. Since the user's preference includes either 8x3090 or 8x4090, I'll lean towards the 8x4090. The selected offer has adequate disk space and reliability, so I’ll proceed with deployment. I'll ensure to manage the environment variables responsibly while running the script.


**Preparing for deployment**

I’m planning to execute the command: `set -a; source .env; set +a; python3 scripts/vast/search_and_deploy.py --deploy ...`. However, I noticed that the .env file has some formatting issues, especially with spaces—there's an invalid line due to missing newlines. Instead of sourcing it, I think it’s better to pass it directly with the script's own dotenv loader using `--env-file .env`. This should avoid any bash errors. I’ll ensure the script has access to the necessary API keys and deploy with the specified parameters. I'll also check the arguments for defaults.


rg -n "add_argument\(\'--image\'|default=.*image|docker_image" -n scripts/vast/search_and_deploy.py


Output:
Exit code: 1
Wall time: 0 seconds
Output:


rg -n "--image" scripts/vast/search_and_deploy.py


Output:
Exit code: 2
Wall time: 0 seconds
Output:
rg: unrecognized flag --image


I'll need to escape properly next time when using the regex command `rg`. It’s important for me to run `rg -n "image"` correctly to get the results I want. It's interesting how small errors can affect my outputs. I should pay closer attention to the syntax to avoid mishaps. Learning from these little mistakes can help me improve overall! I'll make sure to remember this for the future.


rg -n "image" scripts/vast/search_and_deploy.py | head -n 80


Output:
Exit code: 0
Wall time: 0 seconds
Output:
270:    image: str,
283:        "image": image,
327:    p.add_argument("--image", default="bharathkumar192/maya3-pipeline:vast-20260111-cleanup-fix")
385:        print(f"Image: {args.image}")
397:                image=args.image,


sed -n '300,430p' scripts/vast/search_and_deploy.py


Output:
Exit code: 0
Wall time: 0 seconds
Output:
    if not isinstance(instance_id, int):
        print(f"  Unexpected response: {resp}", file=sys.stderr)
        return None

    print(f"  SUCCESS: Instance {instance_id} created", file=sys.stderr)
    return instance_id

def main(argv: Optional[Sequence[str]] = None) -> int:
    p = argparse.ArgumentParser(description="Search and deploy Maya3 to Vast.ai")
    p.add_argument("--api-key", default=None, help="Vast API key")
    p.add_argument("--env-file", default=".env", help="Path to .env file")

    # Search options
    p.add_argument("--search", action="store_true", help="Search for offers (don't deploy)")
    p.add_argument("--deploy", action="store_true", help="Search and deploy")
    p.add_argument("--gpu-types", default="RTX_3090,RTX_4090", help="GPU types (comma-sep)")
    p.add_argument("--num-gpus", type=int, default=8, help="Number of GPUs per machine")
    p.add_argument("--min-inet-down", type=float, default=500.0, help="Min download Mbps")
    p.add_argument("--min-inet-up", type=float, default=100.0, help="Min upload Mbps")
    p.add_argument("--min-disk", type=float, default=400.0, help="Min available disk GB")
    p.add_argument("--min-reliability", type=float, default=0.95, help="Min reliability score")
    p.add_argument("--max-dph", type=float, default=20.0, help="Max $/hour")
    p.add_argument("--deny-countries", default="CN,RU,IR,KP,BY", help="Blocked country codes")

    # Deploy options
    p.add_argument("--count", type=int, default=1, help="Number of instances to deploy")
    p.add_argument("--image", default="bharathkumar192/maya3-pipeline:vast-20260111-cleanup-fix")
    p.add_argument("--disk", type=int, default=500, help="Disk space to allocate GB")
    p.add_argument("--label-prefix", default="maya3")
    p.add_argument("--r2-prefix", default="", help="R2 upload prefix")
    p.add_argument("--dry-run", action="store_true", help="Don't actually deploy")

    args = p.parse_args(list(argv) if argv is not None else None)

    if not args.search and not args.deploy:
        p.print_help()
        return 1

    _load_dotenv(args.env_file)
    api_key = _get_api_key(args.api_key)

    gpu_types = [g.strip() for g in args.gpu_types.split(",") if g.strip()]
    deny_countries = {c.strip().upper() for c in args.deny_countries.split(",") if c.strip()}

    # Search for offers
    offers = search_offers(
        api_key,
        gpu_types=gpu_types,
        num_gpus=args.num_gpus,
        min_inet_down=args.min_inet_down,
        min_inet_up=args.min_inet_up,
        min_disk=args.min_disk,
        min_reliability=args.min_reliability,
        deny_countries=deny_countries,
        max_dph=args.max_dph,
    )

    if not offers:
        print("\nNo offers found matching criteria.", file=sys.stderr)
        print("Try relaxing filters (--min-inet-down, --min-reliability, --max-dph)", file=sys.stderr)
        return 2

    print(f"\n{'='*80}")
    print(f"Found {len(offers)} matching offers:")
    print(f"{'='*80}")
    for i, offer in enumerate(offers, 1):
        print(f"  {i}. {offer}")

    if args.search:
        return 0

    # Deploy
    if args.deploy:
        env_map = _build_env_map(args.r2_prefix)

        # Build onstart command
        onstart = (
            "cd /workspace/maya3_data && "
            "/workspace/maya3_data/.venv/bin/python massive_process.py "
            "--supabase-queue --workers-per-gpu 1 --r2-upload --output ./output"
        )

        print(f"\n{'='*80}")
        print(f"Deploying to {min(args.count, len(offers))} instance(s)...")
        print(f"Image: {args.image}")
        print(f"Disk: {args.disk}GB")
        print(f"{'='*80}")

        deployed = []
        for i, offer in enumerate(offers[:args.count]):
            cc = _country_code(offer.geolocation)
            label = f"{args.label_prefix}-{offer.gpu_name.replace(' ', '')}-{cc or 'XX'}-{offer.id}"

            instance_id = deploy_offer(
                api_key,
                offer,
                image=args.image,
                disk_gb=args.disk,
                label=label,
                env_map=env_map,
                onstart=onstart,
                dry_run=args.dry_run,
            )

            if instance_id:
                deployed.append({
                    "instance_id": instance_id,
                    "offer_id": offer.id,
                    "gpu": f"{offer.num_gpus}x {offer.gpu_name}",
                    "location": offer.geolocation,
                    "dph": offer.dph_total,
                })

            time.sleep(0.3)  # Rate limit

        if deployed:
            print(f"\n{'='*80}")
            print(f"Successfully deployed {len(deployed)} instance(s):")
            for d in deployed:
                print(f"  Instance {d['instance_id']}: {d['gpu']} @ ${d['dph']:.2f}/hr ({d['location']})")
            print(f"\nMonitor with: python scripts/vast/vastctl.py instances")
            print(f"View logs with: python scripts/vast/vastctl.py logs <instance_id>")

    return 0

if __name__ == "__main__":
    raise SystemExit(main())


**Configuring instance parameters**

I'm adjusting the default minimum reliability to 0.90 instead of 0.95. I’ll sort by price, setting my minimum and maximum values to 7200 and 5000, respectively, with a max dph of 3.0. It looks like I should select offer 29914560. Great, I'll proceed to deploy it, and after that, I'll make sure to verify the instance list. Time to execute!


python3 scripts/vast/search_and_deploy.py --deploy --env-file .env --gpu-types RTX_4090 --num-gpus 8 --min-inet-down 7200 --min-inet-up 5000 --min-disk 400 --min-reliability 0.90 --max-dph 3.5 --count 1


Output:
Exit code: 0
Wall time: 1.3 seconds
Output:
Searching for 8x GPUs (['RTX_4090'])...
  Min download: 7200.0 Mbps
  Min upload: 5000.0 Mbps
  Min disk: 400.0 GB
  Min reliability: 0.9
  Blocked countries: {'KP', 'BY', 'RU', 'IR', 'CN'}
  Found 1 raw offers
Deploying to offer 29914560...
  SUCCESS: Instance 29936532 created

================================================================================
Found 1 matching offers:
================================================================================
  1. Offer 29914560: 8x RTX 4090 | $2.88/hr | 7278/5430 Mbps | 2557GB | rel=0.968 | GB

================================================================================
Deploying to 1 instance(s)...
Image: bharathkumar192/maya3-pipeline:vast-20260111-cleanup-fix
Disk: 500GB
================================================================================

================================================================================
Successfully deployed 1 instance(s):
  Instance 29936532: 8x RTX 4090 @ $2.88/hr (United Kingdom, GB)

Monitor with: python scripts/vast/vastctl.py instances
View logs with: python scripts/vast/vastctl.py logs <instance_id>


Summary: auto


Output: Plan updated


python3 scripts/vast/vastctl.py instances | sed -n '1,120p'


Output:
Exit code: 0
Wall time: 0.1 seconds
Output:
Missing Vast API key. Set `VAST_API_KEY` (preferred) or `VAST_AI` in the environment.


**Updating and executing tasks**

I need to show the output, which will include instance IDs but no keys. Also, I should update the plan: step 2 is now completed, and I'm moving on to step 3, which is in progress. I'll execute the necessary actions to ensure everything flows smoothly. Let's keep this organized and make sure I'm tracking all the steps effectively!


Output: Plan updated


python3 - <<'PY'
import os, subprocess, sys
from dotenv import dotenv_values
vals = dotenv_values('.env')
api_key = vals.get('VAST_API_KEY') or vals.get('VAST_AI')
if not api_key:
    print('Missing VAST_API_KEY/VAST_AI in .env', file=sys.stderr)
    raise SystemExit(2)
env = os.environ.copy()
# vastctl checks VAST_API_KEY or VAST_AI
env['VAST_API_KEY'] = api_key
p = subprocess.run([sys.executable, 'scripts/vast/vastctl.py', 'instances'], env=env, text=True, capture_output=True)
print(p.stdout)
if p.returncode != 0:
    print(p.stderr, file=sys.stderr)
raise SystemExit(p.returncode)
PY


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
Instances: 1
- 29936532 | loading | 8x RTX 4090 | ssh1.vast.ai | maya3-RTX4090-GB-29914560


python3 - <<'PY'
import os, subprocess, sys
from dotenv import dotenv_values
vals = dotenv_values('.env')
api_key = vals.get('VAST_API_KEY') or vals.get('VAST_AI')
if not api_key:
    raise SystemExit('missing key')
env = os.environ.copy()
env['VAST_API_KEY'] = api_key
instance_id = '29936532'
p = subprocess.run([sys.executable, 'scripts/vast/vastctl.py', 'logs', instance_id], env=env, text=True, capture_output=True)
# vastctl logs can be huge; print last 80 lines
out = (p.stdout or '').splitlines()
print('\n'.join(out[-80:]))
if p.returncode != 0:
    print(p.stderr, file=sys.stderr)
raise SystemExit(p.returncode)
PY


Output:
Exit code: 0
Wall time: 2.2 seconds
Output:
Error response from daemon: No such container: C.29936532


python3 - <<'PY'
import os, subprocess, sys, time
from dotenv import dotenv_values
vals = dotenv_values('.env')
api_key = vals.get('VAST_API_KEY') or vals.get('VAST_AI')
env = os.environ.copy(); env['VAST_API_KEY']=api_key
for i in range(3):
    p = subprocess.run([sys.executable,'scripts/vast/vastctl.py','instances'], env=env, text=True, capture_output=True)
    print(p.stdout.strip())
    time.sleep(10)
PY


Output:
Exit code: 0
Wall time: 31.2 seconds
Output:
Instances: 1
- 29936532 | loading | 8x RTX 4090 | ssh1.vast.ai | maya3-RTX4090-GB-29914560
Instances: 1
- 29936532 | loading | 8x RTX 4090 | ssh1.vast.ai | maya3-RTX4090-GB-29914560
Instances: 1
- 29936532 | loading | 8x RTX 4090 | ssh1.vast.ai | maya3-RTX4090-GB-29914560


**Updating deployment status**

I'm still in the process of loading the deployment instance, which may take some time. Once it's running, I'll start the workers. I could check the Supabase claimed counts, but they likely won't start until everything is operational. For now, I need to confirm this with the user. I think we should note that step 3 is done and verified, even though it’s still loading. I’ll mention the current status and mark it as completed.


Output: Plan updated


- Deployed 1 Vast cluster: instance `29936532` from offer `29914560` (`8x RTX 4090`, `7278/5430 Mbps`, `GB`) at `$2.88/hr` using image `bharathkumar192/maya3-pipeline:vast-20260111-cleanup-fix`.
- Current Vast status: `loading` (label `maya3-RTX4090-GB-29914560`).

Confirm when you’re ready and I’ll deploy the next batch (e.g., +2 to +4 more) with the same `>=800 Mbps` preference.


# Context from my IDE setup:

## Open tabs:
- 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt: 2026-01-12-this-session-is-being-continued-from-a-previous-co.txt
- .env: .env
- discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv: discovered_podcasts_te_SUPABASE_UNIQUE_50000.csv

## My request for Codex:
also i forgot to mention, we gotta clear the worker table in supabase and other hearbeats analytics in supabase with old cluster details and leave the current one so we can track the analytics of these workers heartbeats and progress. Once cleared, deploy to 4 more instances. look for US based or norway or bulgaria or similar ones. i've had better success with these. remember high internet is also a priority


**Investigating worker_id format**

I’m considering whether to use hostname or the Vast instance. I need to check the `massive_process.py` for the format of `worker_id`. Additionally, I'll look into the monitor/dashboard or the analytics code. We might query Supabase for worker heartbeats as well.

We can utilize `supabase.create_client` in a Python script similar to `reset_for_production.py`, but without clearing videos — just focusing on the analytics tables. There’s a `clear_analytics_tables(dry_run)` function that deletes all rows in those tables. However, the user specified they want to keep the current one, so I need to implement something that deletes rows from other instances while preserving the current one. I’m considering how to determine the `machine_id` based on the `worker_id` pattern, likely derived from its prefix before "_gpu." For migration, the `update_worker_metrics` uses `split_part(NEW.worker_id, '_gpu', 1)` to define `machine_id`, indicating that `worker_id` could look like `machineId_gpu0` and tying this to the Vast instance potentially using `instance_id`. I need to investigate where `worker_id` is created in the code by searching `massive_process.py` for terms like "worker_id" and "gpu."


**Deploying to new instances**

Next, after clearing, I’ll focus on deploying to four more instances in preferred locations like the US, Norway, and Bulgaria, aiming for high internet priority and either 8x 3090 or 8x 4090 configurations. I need to ensure I find matching offers from our earlier search results.

The `search_and_deploy` script allows for deny-countries filtering but not allow-countries, which complicates things a bit. I can’t just hack the script to restrict countries that way. Instead, I’ll need to search for offers and filter by country codes, knowing that the script sorts by price and limits to about 50 offers.

It might work to run the search with high internet and then manually deploy using the `--deploy` option while selecting a count of four. However, I can't restrict countries this way. I can set parameters like `max-dph` and `min-reliability`, but it’d be great to patch the script to accommodate an `--allow-countries` or `--prefer-countries` option with minimal changes.


**Considering deployment options**

I could just do a manual deploy by calling `search_offers`, then picking offers with country codes on my allowed list, deploying them individually with a script that temporarily adjusts deny countries and `max-dph` to exclude other geos. But that seems unreliable.

Maybe the simplest way is to run `--search`, parse the output, and use a script to call `deploy_offer`. I could also explore the `scripts/vast/deploy_from_offers.py` script, which may accept a list of offer IDs for deployment.

I should check for an existing allowlist, and also monitor the current instance to ensure it’s in a "running" state before clearing anything. Clearing analytics is fine before the new cluster starts reporting, but if the current one is still loading, then there might not be any heartbeats yet. It might be best to clear everything now, but it's wise to be cautious about it. We definitely need to clear the relevant worker tables and analytics without deleting the 'videos' statuses.


**Creating an analytics clearing script**

I’m planning to implement a script called `scripts/clear_analytics_except_current.py`, or maybe I'll just use a Python one-liner for now. But I think committing a full script would help with repeatability.

I need to identify the current worker IDs to preserve. I could check `worker_heartbeats` for any that had a last heartbeat within the last 10 minutes, but I’m concerned about stale heartbeats from old clusters. They mentioned leaving the current one, so I’ll focus on that.

If old clusters are still running, they could have recent heartbeats too, but it makes sense that we’d clear details for old clusters since they might be gone now. The simplest option would be to delete all rows from those tables. However, since they specifically asked to leave the current one, I’ll take the safer approach of preserving rows with a last heartbeat within the last 10 minutes or matching the current machine ID.


**Clearing and deploying analytics**

I’m trying to find the best way to clear analytics while preserving necessary data, especially with worker IDs and heartbeats. Using the Supabase API, I can't compute NOW server-side directly. I plan to treat "current" as those with a last heartbeat above a certain cutoff. However, this might also preserve old clusters that the user wants to clear.

I should consider methods to identify the current cluster's machine ID, potentially through the hostname. The hostname structure seems to suggest that it might include the instance ID, which I can verify in `massive_process.py`.

Ultimately, it might be easier to clear all analytics tables, especially since the current instance is still loading and probably has no heartbeats yet. I'll draft a script, `scripts/clear_analytics_tables.py`, to delete all necessary rows from tables while ensuring I don't touch the videos. I’m also considering allowing options for preservation based on heartbeat recency or machine ID strings. Since the user stated they want to track analytics of current/new clusters only after clearing, I can proceed with clearing everything now and then deploy four new instances based on their specifications, verifying worker heartbeat counts beforehand.


**Setting up analytics cleanup**

I need to ensure that the environment variables for the vast API are present since `search_and_deploy` uses a `.env` file by default. There’s a malformed line in the file that might cause issues, so I should be careful about sourcing it directly in bash. Instead, I could read the values in Python using `load_dotenv`. This way, `search_and_deploy` can access necessary variables without trace errors.

For analytics cleanup, I’ll create a new script to delete rows from various tables while ensuring meaningful counts are output before and after the deletion. Given the request to leave the current worker data intact, I should implement a way to preserve recent heartbeats during the cleanup, ideally those within the last 15 minutes. This would help maintain vital information while still clearing old clusters. I plan to start by querying current counts in the analytics tables to gauge the workload involved in the deletion. Then, after clearing, I'll proceed to deploy four more instances with the specified offer filters.


**Planning analytics deletion**

I need to check if the current instance has started since it's been loading for a while. I'll verify if there are any recent `worker_heartbeats` entries, as there might be none or only some from previous clusters if they haven't been cleared. The user wants to clear old cluster details but has asked to leave the current instance intact.

To achieve this, I'll implement a strategy to preserve any recent heartbeats from the last 30 minutes while deleting records from other tables. The algorithm involves querying `worker_heartbeats` for entries that meet the preservation criteria and then conducting batch deletes across the relevant tables, ensuring that I handle situations where there might be too many rows to delete at once.

If the preserved set is notably large, I might opt to clear everything instead. I'll also ensure that while deleting entries, I avoid infinite loops by carefully selecting and managing the IDs. This approach will help keep necessary tracking information while effectively clearing out what is no longer needed.


python3 - <<'PY'
import inspect
from postgrest._sync.request_builder import SyncRequestBuilder
from postgrest._sync.filter_request_builder import SyncFilterRequestBuilder
print('has not_:', hasattr(SyncFilterRequestBuilder,'not_'))
print('dir contains in_?', 'in_' in dir(SyncFilterRequestBuilder))
print('dir contains not_', [m for m in dir(SyncFilterRequestBuilder) if m.startswith('not')][:20])
PY


Output:
Exit code: 1
Wall time: 0.3 seconds
Output:
Traceback (most recent call last):
  File "<stdin>", line 3, in <module>
ModuleNotFoundError: No module named 'postgrest._sync.filter_request_builder'


Summary: auto


python3 - <<'PY'
import inspect
from postgrest._sync.request_builder import SyncRequestBuilder
methods=[m for m in dir(SyncRequestBuilder) if m in ['eq','neq','lt','lte','gt','gte','in_','not_','or_','match','filter','contains','contained_by','like','ilike','is_']]
print('filter-ish methods:', methods)
print('has in_', hasattr(SyncRequestBuilder,'in_'))
print('has not_', hasattr(SyncRequestBuilder,'not_'))
PY


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
filter-ish methods: []
has in_ False
has not_ False


I’m trying to understand if `sb.table()` returns `SyncRequestBuilder`, but it looks like the filters are in `postgrest._sync.filter_request_builder.SyncFilterRequestBuilder` and the path has changed. I think I should inspect the `postgrest._sync` package further to clarify things. I’ll list the modules to see what’s there and how they interact. It’s important to get this right, so let’s dig in!


python3 - <<'PY'
import pkgutil, postgrest
import postgrest._sync as sync
print('postgrest', postgrest.__version__)
print('modules in postgrest._sync:')
for m in pkgutil.iter_modules(sync.__path__):
    print(' -', m.name)
PY


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
postgrest 2.27.0
modules in postgrest._sync:
 - client
 - request_builder


python3 - <<'PY'
import inspect
import postgrest._sync.request_builder as rb
print(rb.__file__)
PY


Output:
Exit code: 0
Wall time: 0.3 seconds
Output:
/home/ubuntu/.local/lib/python3.10/site-packages/postgrest/_sync/request_builder.py


sed -n '1,240p' /home/ubuntu/.local/lib/python3.10/site-packages/postgrest/_sync/request_builder.py