ABC @Ubunta

My @OpenAI API key was somehow compromised, and I became curious: what could someone actually do with a stolen key? The logs led to me the project named Stratum—a genuinely intelligent Rust project aimed at the Docker ecosystem, used gpt-6-astra (ofcourse) Most secret scanners inspect small configuration files. Stratum targets the large 20–500 MB Docker image layers that people rarely unpack and inspect—yet may contain 5–6× more exposed credentials. The engineering is impressive: - Streaming image-layer downloads - RocksDB with column families and prefix-range scans - Snappy and Zstd compression - Cryptographic validation for BIP39, secp256k1, Base58Check, and CRC16 - JWT classification, entropy checks, and placeholder deny-lists - Content-addressed caching to reuse findings across images It does not stop at finding secrets. It can live-test exposed OpenAI and Anthropic keys, read OpenRouter credits, derive wallets from leaked private keys, and check balances across seven blockchains. It even uses a pool of Docker accounts to work around rate limits and scan roughly 700,000 layers per day. Beautiful engineering—but essentially a high-performance loot scanner. 🔥 Now I need to figure out how my key was compromised in the first place! 😅

i checked and realized my vibe code error Correction: @abimaelmartell was right, the 275s was a bug in my method not the library. pages= filters after whole-document work — pages=1 and pages=8 both take ~278s. So "137s/page" and "36 hours" are wrong. Real: 947 pages in 6.9 min vs my 6.2. Pretty Competitive. What stands: 66.6% of numeric values missing from the markdown, and no cell geometry. That's the actual blocker for citing a number in a regulated doc.

I tried pdf-inspector on a 947-page clinical listing to see if it could replace my current PDF table extraction pipeline. For my use case, readable text is not enough. I need to know exactly which table cell a value came from: page, table, row, column, and the exact bounding box. In regulated documents, that matters because a citation should highlight the actual number supporting a claim. My current extractor uses pdfplumber/pdfminer for table structure and PyMuPDF for text and geometry. After some optimisation, the full 947-page extraction dropped from ~55 minutes to 6.2 minutes, while keeping cell geometry and row context. pdf-inspector had one very impressive result: classify_pdf processed all 947 pages in 0.65 seconds and correctly identified the document as text-based. But Markdown extraction was a different story. Two pages took ~275 seconds. Based on that result, a simple projection for 947 pages would be around 36 hours. More importantly, Markdown does not preserve the cell-level provenance I need. I also saw separate table cells getting merged into one Markdown cell. So I would definitely consider pdf-inspector for PDF classification, OCR routing, and document inspection. But for evidence-grade clinical table extraction, I am staying with the current pipeline. Maybe in the future, will test Per-cell geometry and much faster page extraction.

Link github github.com/firecrawl/pdf-…

First Claude went down, so I switched to Codex. Then Codex went down too. 😅

Claude is back to basics and down again!

I wanted to see how far a small open-weight model can go on regulated data workflows if I give it skills instead of moving to a bigger model. Setup: 27B dense model at Q6, 2 consumer GPUs, fully local, no fine-tuning. 5 SKILL md files: cohort specs, value sets, index/washout logic, bias review, ledger classification. Constrained JSON output. Every code, count and number comes from a tool call, not from the model. I have not tested it deeply yet. But what I saw is good enough to keep going. Most of the improvement seems to come from the skill and context layer. The main problem so far is the wrong skill getting loaded. I think adding skills might be enough for a lot of these tasks. Worth trying before moving to a bigger model.

So technically, @duckdb is joining AWS… and now I’m genuinely not sure how to react 😅

DuckDB @duckdb

4 weeks ago

Today, @ducklabs_com is joining @awscloud. The move is expected to be completed by early September. Joining AWS gives DuckLabs the resources and reach to bring DuckDB, DuckLake, and the Quack protocol to many more developers and organizations – and to pursue ideas at a scale

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Had Claude Code run Codex as an independent reviewer. Read-only sandbox, full session log — every command it ran, replayable after the fact. Good: I can prove exactly what it looked at. Less good: that proves provenance, not correctness. Still had to verify the findings myself.

Claude CLI remote control complexity is almost zero — one command and it mostly just works. Comparatively Codex remote control feels very old-school Windows Vista 😅 Too many steps around start, pair, connect, and you still don’t really know if it will work this time or not. For something like remote control, I would prefer much less process and much more predictability.

I am extremely impressed by Qwen-3.8 for Agentic Coding, so I am setting up a GPU System for Local LLMs — and somehow that has become one of my core reasons to travel to India - 2× used RTX 3090 24GB. They are way cheaper in India, and if I really need more VRAM I can add a second one later. - Ryzen 5600 + AM4 + 64GB DDR4 is cheaper in Germany. - Will still size the motherboard + PSU for 2×3090, so upgrading later is easy. - Energy cost is way cheaper in India, even after considering air conditioning. For remote access, I will use an encrypted WireGuard/Tailscale tunnel. Upload speed from India is also not that bad, and I have already tested the same setup with my 3060 sitting there — network was honestly not the biggest hurdle. Currently my 2013 Mac running Linux handles the GPU server remotely, and I hope to continue using it as the little management box 😅 Might also add PiKVM + UPS, because debugging a GPU server from 6,500 km away is probably where things get interesting.

Link chromewebstore.google.com/detail/trendab…

My old new-tab extension died, so I vibe coded my own. It's on the Chrome Web Store. Quietly pleased about this one It puts GitHub trending, Hacker News, Reddit, Lobsters and arXiv in one tab. The part I use constantly: click anything and just ask. A repo with 2,000 stars today — what is it actually for? A Hacker News thread with 400 comments — what did people land on? I used to open the thread and skim for ten minutes. Now I get it in the same tab, without leaving. Your own API key. No backend.

Link chromewebstore.google.com/detail/trendab…

Read a scaling-law post today and finally got a frame that stuck. Think of a model like a student. You can grow the brain, hand them more books, give them longer per question, or put them with a tutor. Brain size is shelf space. It's how much the student holds, not how many steps of reasoning they carry before losing the thread. Dense models blurred those together. MoE splits them into separate numbers. Then economics. The student doesn't sit one exam, they sit millions and you pay every time. That's why the open-weight releases keep training small models way past the point that looks sensible. Expensive once, cheap forever after. And the recent ones make the last dial obvious. Same base, same architecture, same parameter count as the previous version. More post-training. Real gains. Scaling AI Models has multiple dials. They don't have to move together.

Claude is down again, and it’s happening way too often

I had to run an LLM over a lot of clinical notes. Sensitive data, so hosted APIs were out. Local model, our own GPUs. The model part was easy. Sharing GPUs was the annoying part. A few teams use the same machines, everyone seems to start around 9am, and one bad run can easily block someone else's fine-tune. I looked at Ray, Kubernetes, even thought about putting together a small Celery + Redis queue. Then someone suggested Slurm. I always thought of Slurm as an HPC/university cluster thing, but it actually fits this problem really well. slurm.schedmd.com/overview.html You submit a job, say what resources you need, and Slurm handles who gets what and when. The nicest surprise was sacct. It already keeps a history of the jobs that ran. So when QA asked who ran what, where, for which study, and whether it completed, most of that was already there. No need to build another tracking system around it. You can also keep sensitive workloads on specific nodes using partitions, and run hooks before/after jobs for mounting, cleanup, GPU reset, etc. Slurm was quite new to me, so I can't really compare it with Ray yet. I also think they solve slightly different problems. But for this shared GPU setup, I really liked Slurm. Much more useful than I expected.

If you're building anything that touches HIPAA or sensitive data, look at Presidio: github.com/microsoft/pres… You feed it text, it finds the identifiers and swaps them for tokens, and keeps the mapping on your side. Find PHI, replace, re-identify later — the whole machine in one library. I tried the DIY route first — regex plus spaCy NER. It works, but it leaks. Presidio packages the same approach with context rules and custom recognizers on top, and the leakage drops noticeably.

Vibe coding makes it much faster to ship features. But just because I can build 10 features faster doesn’t mean customers suddenly have time or interest to test all 10. Shipping got faster. Customer attention didn’t. So if we keep building at AI speed without enough validation, we are basically just guessing faster. Fast doesn’t mean useful.

I seriously considered building a local LLM setup for coding on sensitive data. I actually wanted the idea to work. Mac Studio M3 Ultra with enough memory to run something useful: around €5K-€8K. Expensive, but I knew that going in. What I hadn't thought about was where it lives. It runs hot and loud, so it doesn't sit next to me while I work. In Germany that quietly becomes an extra room, which costs more than the machine. Then I remembered electricity. 8 hours of inference a day is about €20 - €40 a month. Fine. But that's not how I'd use it. Agents would keep it running all day. That's €50-€80, plus cooling in summer. And electricity still isn't the real problem. After €7K, the heat and possibly more rent, I'd be running a smaller, slower model than what I already get hosted. So I looked at the next level. RTX Pro 6000, 600W GPU, ~850W at the wall. €90-€110 a month at eight hours daily, closer to €270 if it stays properly loaded and cooled. The box costs more than the Mac before any of that. Local still makes sense for experiments and for work where data genuinely cannot leave the building. As my everyday coding setup, it doesn't work.