2026-02-10 07:56:51
Structured Context Engineering for File-Native Agentic Systems
New paper by Damon McMillan exploring challenging LLM context tasks involving large SQL schemas (up to 10,000 tables) across different models and file formats:Using SQL generation as a proxy for programmatic agent operations, we present a systematic study of context engineering for structured data, comprising 9,649 experiments across 11 models, 4 formats (YAML, Markdown, JSON, Token-Oriented Object Notation [TOON]), and schemas ranging from 10 to 10,000 tables.
Unsurprisingly, the biggest impact was the models themselves - with frontier models (Opus 4.5, GPT-5.2, Gemini 2.5 Pro) beating the leading open source models (DeepSeek V3.2, Kimi K2, Llama 4).
Those frontier models benefited from filesystem based context retrieval, but the open source models had much less convincing results with those, which reinforces my feeling that the filesystem coding agent loops aren't handled as well by open weight models just yet. The Terminal Bench 2.0 leaderboard is still dominated by Anthropic, OpenAI and Gemini.
The "grep tax" result against TOON was an interesting detail. TOON is meant to represent structured data in as few tokens as possible, but it turns out the model's unfamiliarity with that format led to them spending significantly more tokens over multiple iterations trying to figure it out:
Via @omarsar0
Tags: ai, prompt-engineering, generative-ai, llms, paper-review, context-engineering
2026-02-10 00:43:07
AI Doesn’t Reduce Work—It Intensifies It
Aruna Ranganathan and Xingqi Maggie Ye from Berkeley Haas School of Business report initial findings in the HBR from their April to December 2025 study of 200 employees at a "U.S.-based technology company".This captures an effect I've been observing in my own work with LLMs: the productivity boost these things can provide is exhausting.
AI introduced a new rhythm in which workers managed several active threads at once: manually writing code while AI generated an alternative version, running multiple agents in parallel, or reviving long-deferred tasks because AI could “handle them” in the background. They did this, in part, because they felt they had a “partner” that could help them move through their workload.
While this sense of having a “partner” enabled a feeling of momentum, the reality was a continual switching of attention, frequent checking of AI outputs, and a growing number of open tasks. This created cognitive load and a sense of always juggling, even as the work felt productive.
I'm frequently finding myself with work on two or three projects running parallel. I can get so much done, but after just an hour or two my mental energy for the day feels almost entirely depleted.
I've had conversations with people recently who are losing sleep because they're finding building yet another feature with "just one more prompt" irresistible.
The HBR piece calls for organizations to build an "AI practice" that structures how AI is used to help avoid burnout and counter effects that "make it harder for organizations to distinguish genuine productivity gains from unsustainable intensity".
I think we've just disrupted decades of existing intuition about sustainable working practices. It's going to take a while and some discipline to find a good new balance.
Via Hacker News
Tags: careers, ai, generative-ai, llms, ai-assisted-programming, ai-ethics
2026-02-09 01:25:07
Friend and neighbour Karen James made me a Kākāpō mug. It has a charismatic Kākāpō, four Kākāpō chicks (in celebration of the 2026 breeding season) and even has some rimu fruit!
I love it so much.
2026-02-08 10:25:53
People on the orange site are laughing at this, assuming it's just an ad and that there's nothing to it. Vulnerability researchers I talk to do not think this is a joke. As an erstwhile vuln researcher myself: do not bet against LLMs on this.
Axios: Anthropic's Claude Opus 4.6 uncovers 500 zero-day flaws in open-source
I think vulnerability research might be THE MOST LLM-amenable software engineering problem. Pattern-driven. Huge corpus of operational public patterns. Closed loops. Forward progress from stimulus/response tooling. Search problems.
Vulnerability research outcomes are in THE MODEL CARDS for frontier labs. Those companies have so much money they're literally distorting the economy. Money buys vuln research outcomes. Why would you think they were faking any of this?
Tags: thomas-ptacek, anthropic, claude, security, generative-ai, ai, llms, open-source
2026-02-08 07:57:57
Mitchell Hashimoto's new system to help address the deluge of worthless AI-generated PRs faced by open source projects now that the friction involved in contributing has dropped so low.
The idea is simple: Unvouched users can't contribute to your projects. Very bad users can be explicitly "denounced", effectively blocked. Users are vouched or denounced by contributors via GitHub issue or discussion comments or via the CLI.
Integration into GitHub is as simple as adopting the published GitHub actions. Done. Additionally, the system itself is generic to forges and not tied to GitHub in any way.
Who and how someone is vouched or denounced is up to the project. I'm not the value police for the world. Decide for yourself what works for your project and your community.
Tags: open-source, ai, github-actions, generative-ai, mitchell-hashimoto, ai-ethics
2026-02-08 07:10:33
Claude: Speed up responses with fast mode
New "research preview" from Anthropic today: you can now access a faster version of their frontier model Claude Opus 4.6 by typing/fast in Claude Code... but at a cost that's 6x the normal price.
Opus is usually $5/million input and $25/million output. The new fast mode is $30/million input and $150/million output!
There's a 50% discount until the end of February 16th, so only a 3x multiple (!) before then.
How much faster is it? The linked documentation doesn't say, but on Twitter Claude say:
Our teams have been building with a 2.5x-faster version of Claude Opus 4.6.
We’re now making it available as an early experiment via Claude Code and our API.
Claude Opus 4.5 had a context limit of 200,000 tokens. 4.6 has an option to increase that to 1,000,000 at 2x the input price ($10/m) and 1.5x the output price ($37.50/m) once your input exceeds 200,000 tokens. These multiples hold for fast mode too, so after Feb 16th you'll be able to pay a hefty $60/m input and $225/m output for Anthropic's fastest best model.
Tags: ai, generative-ai, llms, anthropic, claude, llm-pricing, claude-code
