Your agent session transcripts are precious, keep them
AI vendors collect every coding session they can, yet most teams discard their own. Quesma Shipper collects them into Amazon S3.
Benchmarks, token economics and research on AI coding agents
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HN I burned all my tokens researching how to save tokens
How I burned a full Claude limit in 30 minutes and built my own deep research pipeline instead: 3 subscriptions, shared memory, a clear role for each model. You can build the same from what you already pay for.
HN Qwen3.6 27B is the sweet spot for local development
Qwen3.6 27B is finally a smart model we can use for coding on MacBook or NVIDIA RTX - with llama.cpp and OpenCode.
HN We hid backdoors in ~40 MB binaries and asked AI + Ghidra to find them
BinaryAudit benchmarks AI agents using Ghidra to find backdoors in compiled binaries of real open-source servers, proxies, and network infrastructure.
Nano Banana Pro: raw intelligence with tool use
Finally, an AI that can draw a map or create an infographic. The capability of leveraging tools pushed the frontier of image generation.
HN The security paradox of local LLMs
Local LLMs prioritize privacy over security. Our research reveals a 95% backdoor injection success rate.
HN CompileBench: can AI compile 22-year-old code?
We tested 19 LLMs on their ability to handle real-world software engineering tasks like compiling old code and cross-compiling. See how Anthropic, OpenAI, and Google models stack up in our new benchmark – CompileBench.
HN Tau² benchmark: how a prompt rewrite boosted GPT-5-mini by 22%
We expected small models to be fast, but our benchmarks revealed a common reliability trap. Here’s our deep dive on finding and fixing it.