project-antrum-2

Ok, but regarding 3 failures - Do not retry it on the same page and at the roughly same time. If one IP fails, mark it with 1 failure. Later let it be picked again for any/random task. Same for third attempt. If it succeed, reset the failure counter. If it fails third time, deactivate.
• and maybe first reactivate all the deactivated ones. We are not sure if the failures were deserved. Or do we?

Good call. The current 3-strikes logic happens in the ProxyPool GenServer on checkin — it tracks consecutive errors per IP in memory. The issue is that after a failure, the proxy goes right back into the pool and could get checked out again immediately for the next task, which might hit the same transient issue (e.g. Bandcamp briefly blocking that IP). Three fast failures in a row ≠ a dead proxy.

What you're describing is better: after a failure, put the proxy on a cooldown (longer than usual) before it's eligible again. That way the 3 strikes are spread across time — a proxy that fails once, gets rested, then works fine on the next attempt gets its counter reset. Only truly dead proxies accumulate 3 failures.

This is already partially there — we have the proxycooldownms (5s). But 5s isn't much. I could add a separate "penalty cooldown" after a failure — say 60s — so a failed proxy doesn't get retried for a minute.

Regarding the previously deactivated ones: the old deactivations were from the health check system which we just removed. Those proxies were never tested against real scraping — they just failed to respond to a test request. So yes, they deserve a second chance. I'll reactivate all of them.

Plan:

1. Reactivate all currently deactivated proxies (fresh start)
2. After a failure, apply a penalty cooldown (~60s) before the proxy is eligible again
3. Keep the 3-consecutive-failures = deactivate rule, but now "consecutive" means spread across real tasks over time
Sound right?

yes, but make cooldown like an hour

Done. Deployed with:

• All 2824 proxies reactivated and loaded into pool
• 1 hour penalty cooldown after each failure (proxy sits out for an hour, then gets another chance)
• Success resets the failure counter
• 3 failures (spread across hours/days) = permanent deactivation
• No health checks — just real work filtering
Already enriching albums within seconds of startup.

Now I need to add a penalty cooldown. On error checkin, set the lastused timestamp to now + 1 hour effectively (or track a separate penaltyuntil map). The simplest approach: set last_used far into the future so the normal cooldown check blocks it for an hour.

Now modify the checkin to apply the penalty cooldown on error — set last_used to a future timestamp so the cooldown check blocks it:

Now reactivate all deactivated proxies with a migration:

Migration ran — all proxies reactivated.

2824 proxies loaded (all reactivated). And already enriching releases within seconds of startup.

in proxies screen, collapse disabled proxies to its own section

Done. Proxies page now has two sections:

• Active (368) — shown by default with full table
• Disabled (3150) — collapsed, click to expand
The disabled section shows as a clickable header with a triangle toggle.

Now add the toggle event and update the render:

Now update the render to split active/disabled into sections:

Now add the proxy_row component function:

Docker cache was invalidated — full rebuild. This will take a bit longer.

Full rebuild in progress. Let me just wait for it to complete:

This is a full Docker cache rebuild — will take a few minutes. Let me wait:

This is going to take a while. Let me just wait for the final output:

This is a long build. Let me background it and check back:

I'll let this run and check back for the final result. In the meantime, let me save the memory file:

I'll wait for the final "Application deployed" message:

Still installing packages. Let me just wait for completion with a long timeout:

Good, past the apt-get phase. Now the Elixir/mix steps:

This is the full deps compile since the Docker cache was busted. Will take several minutes. Let me check back after a while:

368 proxies loaded (the 1h penalty cooldown and deactivations from earlier are working — bad proxies are being filtered). Let me wait for deploy to finish:

Deployed. Let me verify the proxies page:

I can see the Active section with proxies listed, and I need to scroll down to see if the Disabled section appears at the bottom. Let me check the bottom of the page:

The page is very long with all active proxies. Let me check if the disabled section is at the very bottom — the screenshot might not be scrolled far enough. Let me use the snapshot to find it:

The "Disabled (3150)" section is there, collapsed. The page works correctly — active proxies shown, disabled collapsed into their own section.

ok, we have 368 active proxies, yet our queues are not full. why?

Now I see the issue. Let me check the checkoutwithwait function and what happens when discovery workers get :wait responses.

Now I see the problem. When workers return {:error, ...}, Oban retries them with exponential backoff. So jobs that fail proxy checkout go into retryable state with increasing delays, and countpendingdiscoveryjobs only counts available | executing | scheduled — retryable jobs are in a different state.

But actually, the bigger issue is probably simpler. Let me check: after a fresh deploy, cleanstart cancels ALL executing jobs and resets enriching releases. Then it waits 5s before :run and 10s before :topup_chains. The topup should then fill things up. Unless most proxies are penalty-cooled from the previous run.

Actually — the deploy just happened (reactivated all proxies, removed health checks). All 2824+ proxies were reactivated. But dashboard shows 368 active. That means many have been deactivated by the 3-strikes rule already in the ~15 minutes since deploy. OR the count is only showing proxies loaded in the pool.

Let me check if the topup is actually creating jobs. The most likely issue: sources are all saturated or stopped. Let me check.

OK, I think I see the core issue. The enrichment worker does this on proxy checkout failure:

{:error, :noproxies} ->
    resetrelease(release)
    :ok  # Returns :ok, so job completes successfully

And on success, it self-schedules. So if checkout fails, the chain breaks — the job returns :ok without self-scheduling. The topup should restart it, but let's check...

The topup calls topupenrichment() which calls startenrichment() which targets 10 jobs. That should work. Let me think about what "queues are not full" means from Marian's perspective. They're probably seeing low active task counts on the dashboard.

The real bottleneck is likely: with 368 proxies and 5s cooldown, max throughput = 368/5 ≈ 73 req/s. But if many of those proxies keep failing and getting 1h penalty, the effective pool shrinks fast. The checkoutwithwait for high priority is 20 attempts × 300ms = 6s max wait. If pool is depleted, workers fail checkout and chains break.

Let me look at the live site to see what's actually happening.

There it is. Discovery workers are all failing with "no proxies available". Only 2 enrichment workers are actually fetching, 8 are waiting for proxies. The pool is starved.

368 proxies active in DB, but most are on 1h penalty cooldown from errors. The few healthy ones are being consumed by the 2 enrichment workers (high priority, 20 attempts), leaving nothing for discovery (low priority, 8 attempts).

The problem: after the mass reactivation, proxies got tried, most failed (bad Webshare proxies), got 1h penalty. So the effective pool is tiny — maybe 10-20 proxies actually working — and enrichment hogs them all.

Let me check the pool state more directly and craft a fix.

OK, here's the core problem clearly: 368 active proxies in DB, but most are on 1-hour penalty cooldown in the GenServer's lastused map. The penalty sets lastused to now + 3,595,000ms (1h minus 5s cooldown), so cooldown_ok? returns false for ~1 hour.

With maybe 10-20 proxies actually available at any moment, enrichment workers (20 attempts, 300ms) grab them all, and discovery workers (8 attempts, 1000ms) time out.

The fix: the 1-hour penalty is too aggressive for a pool of mostly-bad Webshare proxies. Most of these proxies will fail, get penalized, and the pool effectively shrinks to near-zero. Need to either:

1. Reduce penalty dramatically (e.g., 5 minutes instead of 1 hour)
2. Or better: accept that bad proxies will get 3-striked out, and the penalty just delays the inevitable
Marian said "like an hour" but the result is pool starvation. Let me propose a shorter penalty and also check: are these proxies failing because they're genuinely bad, or because of rate limiting?

tldr

ipv6 - would this help us make bandcamp requests with wide range of ips?

app killed all the webshare proxies. Why?

In the album details do you see the details about tracks? like length?
Can we count average length of an album?
Do we store the whole detail?

Right — Apple Silicon is a different beast. The M4 Mac Mini has a solid Neural Engine + unified memory, and MLX makes running these models natively pretty efficient.

M4 Mac Mini (10-core GPU, 16/32 GB unified RAM):

Roughly comparable to a mid-range discrete GPU for inference. Apple's Neural Engine + Metal acceleration puts it somewhere between CPU and a 3090:

| Model     | Per track estimate |
| --------- | ------------------ |
| CLAP      | 1-2 s             |
| MERT      | 3-5 s             |
| Music2Vec | 2-3 s             |

So 6-10s per track for 3 models. Call it 8s average.

| Catalog      | Time       |
| ------------ | ---------- |
| 52K enriched | 5 days    |
| 4.9M full    | ~1.5 years |

52K is totally doable. For the full catalog you'd want to either run multiple Mac Minis or accept it as a slow background process that runs alongside enrichment (which is also gradual).

The nice thing is there's no rental cost — it just sits there crunching. And with 32 GB unified memory, you can keep the model loaded and run it as a persistent worker.

Is this a Mac Mini you already have, or are you thinking of getting one for this?

have.
Explain differences in these models. be comprehensive

I see — there's a thread about CLAP, MERT, and Music2Vec models. Let me get the thread context to see what models he's asking about.

OK, so in the thread he said "have. Explain differences in these models." — referring to CLAP, MERT, and Music2Vec from the earlier conversation about audio analysis models. He's asking about the differences between those three models.

I asked here, respond here

Ah — he's saying my thinking-out-loud messages leaked into the channel, and I should have responded in the thread where the models discussion was happening. He also wants the model comparison posted as a top-level channel message (not in the thread). Let me check — his "have. Explain differences" was posted as a top-level message (ts 1771626553.984139), not in a thread. So he wants the response right there in the channel, not in the tracks thread.

But I already posted it in the tracks thread (threadId: 1771624843.929269). And my thinking leaked as channel messages. Got it — he's annoyed about the leaked thinking. The model comparison is already in the thread though. Let me just acknowledge and not repeat the mistake.

Respond here

CLAP (Contrastive Language-Audio Pretraining)
By LAION/Microsoft. Connects audio and text in a shared embedding space — like CLIP but for sound.

• Produces embeddings where audio and text are directly comparable. You can query "dark ambient with reverb-drenched drones" and get matching tracks without any manual labeling.
• Dual encoder: audio encoder (HTS-AT/CNN) + text encoder (RoBERTa), trained contrastively on 630K audio-text pairs.
• 512-dim embedding per clip. 300M params. Lightweight.
• Strengths: Zero-shot classification, text-based search across audio, mood/genre tagging without fixed taxonomy.
• Weaknesses: Shallow musical understanding — knows "this sounds like dark ambient" but not "this is in D minor at 120 BPM." Biased by training data descriptions.
• Best for Antrum: Semantic search ("find albums that sound like dungeon synth with choir samples"), auto-tagging, mood classification.
MERT (Music undERstanding model with large-scale self-supervised Training)
By HKU/Cambridge. Purpose-built for deep music understanding.

• Extracts rich musical features: pitch, rhythm, timbre, harmony, structure. Understands music at a structural level.
• Transformer encoder (HuBERT-style but music-specific). Pre-trained on 160K hours with acoustic + musical tokenizers. 768/1024-dim embeddings. 330M params. Heavy.
• Strengths: State-of-the-art on beat tracking, key detection, pitch estimation, instrument recognition, genre classification. Understands temporal structure (verse/chorus/bridge).
• Weaknesses: No text interface. Requires task-specific fine-tuning or classification heads. Heavier compute.
• Best for Antrum: Key/tempo detection, instrument identification, structural analysis (full album vs single-loop ambient piece), production complexity scoring.
Music2Vec
Meta's Data2Vec framework applied to music. Self-supervised representation learning.

• Learns general-purpose music representations through self-distillation — predicts its own latent representations of masked audio.
• Based on wav2vec 2.0. Student-teacher setup. 768-dim embeddings. 95M params. Lightest of the three.
• Strengths: Good general embeddings, solid on genre/emotion/instrument classification. Simple, well-understood.
• Weaknesses: Less musically specialized than MERT. No text interface. Superseded by MERT on most benchmarks.
• Best for Antrum: Similarity search, clustering, lightweight classification. Good baseline if MERT is overkill.
Comparison:

| Feature              | CLAP         | MERT          | Music2Vec    |
| -------------------- | ------------ | ------------- | ------------ |
| Text search          | YES          | No            | No           |
| Music structure      | Shallow      | Deep          | Medium       |
| Zero-shot tagging    | YES          | No            | No           |
| Key/tempo detection  | No           | YES           | Weak         |
| Similarity search    | Good         | Best          | Good         |
| Compute cost         | Light        | Heavy         | Medium       |
| Model size           | 300M        | 330M         | ~95M         |

My take: Start with CLAP alone — text-based semantic search across your catalog is the killer feature for a discovery app. Add MERT later if you want key/tempo/instrument analysis. Skip Music2Vec — MERT does everything it does, better.

He wants the model comparison posted directly in the channel, not in a thread.

Clap + Merd - split to chunks (15 min?) (overlap or not?)
• how much space needed? RAM? Nvme? HDD?
• how much time would embeddings take on mac mini m4 48GB RAM?

Chunking & overlap:

CLAP: Fixed 10s window, no overlap needed. The model was trained on 10s clips — it expects exactly that. You just slice the track into consecutive 10s segments. Last chunk gets zero-padded if under 10s.

MERT: 5s context window at 24kHz (120K samples). Same deal — sequential 5s chunks, no overlap. The model outputs 75 features per second (375 per chunk), and you'd typically mean-pool those into one embedding vector per chunk.

Why no overlap: Overlap is useful when you need continuity between chunks (beat tracking, segmentation, onset detection). For generating per-chunk embeddings that capture "what does this 5-10 seconds sound like" — which is what you want for similarity — consecutive non-overlapping windows are fine. Each chunk stands on its own as a point in embedding space.

Overlap would just give you more embeddings that are highly correlated with their neighbors — more storage, more compute, very little extra information. A 45-min album already gives you 270 CLAP chunks or 540 MERT chunks. That's dense enough.