cowartc

8 ms·

1.
by cowartc 5mo ago
The headline leads with contamination, but buried is that 59% of audited failures had test design defects. That's a measurement system never validated against ground truth before being adopted industry-wide as a score that mattered.
2.
by cowartc 5mo ago
PCW clustering around ~85-95% regardless of usage is a measurement bias, not a real signal. In manufacturing, this would fail measurement system analysis by having a larger variation than you're trying to detect. Companies trying to
3.
by cowartc 5mo ago
The verifier isn't just a fraud detector. It's an admission that open weights alone aren't a shippable contract. Without a standardized verifier, a buyer has no way to know which case they're in. The weights are the e
4.
by cowartc 5mo ago
Interesting direction. One question: How does this hold up outside the synthetic transformer on a real downstream task? Reconstruction error is the right measure but its one step removed from the end task. I'm curious whether HAE wo
5.
by cowartc 5mo ago
The real rate is certainly higher because this only catches the laziest form of error. The harder problem is the same one we see in production ML. Your pipeline can produce confident results on garbage data and nothing in the system tells
6.
by cowartc 5mo ago
This is a symptom of the problem. The real issue is that everyone is running off and building their own thing without tying back to a north star and coordinating. I've seen this play out before in a F200. Tooling proliferation resolve
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Probabilistic Record Linkage Using Pretrained Text Embeddings(cambridge.org)
1 points by cowartc 5mo ago|0 comments
8.
by cowartc 5mo ago
Hallucination vs real finding distinction is the core problem and doesn't get solved by a better model alone. It gets solved by what you do with the output. The verification layer is what makes the system production grade.
9.
by cowartc 5mo ago
The scarcity framing assumes compute is the bottleneck. For most production deployment's Ive seen, the actual bottleneck is evaluation and knowing what to trust. You can throw cheaper models at a problem all day but, if you can't
10.
by cowartc 5mo ago
This is what I found doing playwright based extraction against anti-bot defenses. Runtime agents were brittle. It felt like trying to debug/audit a black box. We used to deal with RPA stuff at work. Always fragile. Good to see evol
11.
by cowartc 5mo ago
The separation of harness from compute is the right architectural move. The part that's still missing from most agent frameworks is the verification layer between steps. Sandbox execution solves the safety problem. It doesn't
12.
by cowartc 5mo ago
There's truth in the accountability angle, but the architectural driver is cost. Three vendor layers with human queues at every handoff is expensive. A confidence gate that routes 70% of decisions to automation and only escalation on
13.
The Three Enterprise Layers Are Collapsing into One(walsenburgtech.com)
3 points by cowartc 5mo ago|2 comments
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Quantization, LoRA, and the 8% Problem Benchmarking Local LLMs for Production AI(walsenburgtech.com)
3 points by cowartc 5mo ago|0 comments
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KYB Engine at 3 Quantization Levels: Accuracy Held. Cost Dropped 6x(walsenburgtech.com)
1 points by cowartc 5mo ago|0 comments
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Show HN: Same agentic pipeline, two implementations – custom async vs. LangGraph(walsenburgtech.com)
2 points by cowartc 6mo ago|0 comments
17.
by cowartc 6mo ago
Location: Walsenburg, CO Remote: Yes (US only) Willing to relocate: No Technologies: Python, Java, TypeScript, Kafka, Spark, PostgreSQL, OpenSearch, AWS (Lambda, ECS, S3, Step Functions), Kubernetes, Terraform, MLflow, PyTorch, FastAPI, Ben