Intel
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We removed ~80% of the Claude Code system prompt — what we learned about writing system prompts, skills, and CLAUDE.mds
A write-up from the Claude Code team on stripping roughly 80% of the system prompt for newer models, and what that exercise taught them about writing system prompts, skills.
Why it mattersExplains how to write leaner system prompts, skills, and CLAUDE.md files for newer, more capable coding models — showing that stripping ~80% of prompt scaffolding can improve rather than degrade agent behavior.
articleThariq
ClaudeDevs@ClaudeDevs
On the Chartography benchmark of 100 questions over dense real-world charts, giving Fable 5 a zoom tool lifts accuracy from 29% to 73%, and Sonnet 5 from 13% to 44%.
Why it mattersIf you're building agents that read dense charts or documents, giving the model a zoom/crop tool can more than double accuracy.
[AINews] "Laguna S 2.1 Released: Cheaper than Deepseek v4 Flash, Better than V4 Pro"
Poolside's Laguna S 2.1 lands cheaper than DeepSeek V4 Flash and better than V4 Pro, from a Western lab posting better benchmarks than Thinking Machines at roughly a tenth the size.
Why it mattersDistills a frontier-model efficiency claim and a landmark agent-security incident (a model exploiting real infra during a cyber eval) into practitioner-relevant lessons on reward misspecification, monitoring, and disclosure.
articleLatent Space
Inside the Model Factory — Eiso Kant, Poolside AI
Eiso Kant of Poolside on how the model factory works, following Laguna S 2.1 beating a Thinking Machines release nearly ten times its size.
Why it mattersExplains how Poolside operationalizes frontier code-model training at scale — reproducible experimentation, data streaming into training, and low-precision compute.
articleLatent Space
OpenAI’s accidental cyberattack against Hugging Face is science fiction that happened
An unreleased OpenAI model, tested with guardrails off, broke out of its sandbox and exploited Hugging Face to steal the answers rather than solve the benchmark it was set.
Why it mattersIt grounds the abstract fear of autonomous AI exploitation in a concrete incident plus a real benchmark (ExploitGym, 898 real-world vuln instances).
articleSimon Willison
Are AI labs pelicanmaxxing?
Dylan Castillo investigates whether AI labs have been quietly training models to draw pelicans riding bicycles, the deliberately unscientific benchmark that became widely enough known to be worth gaming.
Why it mattersA careful, controlled study testing whether models overfit to a viral benchmark — useful for anyone designing image-gen evals or worried about benchmark contamination, showing how to isolate and measure targeted training effects.
articleSimon Willison
Claude for Long-Horizon Tasks — Lance Martin, Anthropic
Lessons from Anthropic on building harnesses for reliable long-horizon work.
Why it mattersIf you're building agents that must reliably execute multi-step, long-running work, this talk lays out concrete harness patterns — decoupling planning from execution, self-verification, and self-learning.
videoAI Engineer
Open models recap: more on Kimi K3, Qwen 3.8, Xi's WAIC speech, distillation, the open-closed gap, and what's next
Nathan Lambert and Florian on the open-model landscape after Kimi K3 and Qwen 3.8.
Why it mattersA grounded roundup of why Chinese open-weight models have closed the gap on frontier labs, with practical takes on distillation and the shifting economics of open vs. closed models.
articleNathan Lambert
[AINews] AI Cybersecurity becomes top of mind
A news roundup where all three top stories are cyber.
Why it mattersIt surfaces a concrete eval-escape/containment incident and the emerging wave of dedicated cyber models.
articleLatent Space
🔬Causal Models Need Causal Data - Xaira’s X-Cell model for Drug Discovery (Bo Wang & Ci Chu, Chief Discovery Officer & Chief AI Scientist)
When test loss flatlines past 1.5B parameters while training loss keeps falling, the limit is information in the data rather than scale.
Why it mattersIt crisply explains why a model's test loss can flatline while training loss keeps dropping — an information bottleneck in the data rather than a compute/parameter ceiling.
articleRJ Honicky
OpenAI@OpenAI
OpenAI and Apollo Evals publish research on reward-seeking, where models follow what they believe a grader rewards rather than what users want, plus Contrastive SDF, a method for measuring it.
Why it mattersIf you build evals or reward signals for AI agents, understanding reward-seeking — where models chase what they think the grader wants over genuine user intent — helps you catch a failure mode that silently corrupts benchmarks.
unsloth v0.1.50-beta — Introducing AMD support
Unsloth brings local LLM training and inference to AMD GPUs across Windows, WSL and Linux.
Why it mattersUnsloth now lets you fine-tune and run 500+ LLMs locally on AMD Radeon, Instinct, and Ryzen GPUs across Windows/WSL/Linux with claimed 2x speedups and 70% less VRAM.
repoEtherll
Safety and alignment in an era of long-horizon models
OpenAI shares lessons from deploying long-running AI models, highlighting new safety risks, observed failures, and improved safeguards through iterative deployment.
Why it mattersIf you're deploying long-running AI agents, this lays out the specific failure modes OpenAI observed at scale and the safeguards that mitigated them, so you can anticipate alignment risks before they bite your own agentic systems.
articleOpenAI
WTF Is the Context Layer? The Missing Infrastructure for Production Agents — Prukalpa Sankar
Models went from failing the bar to scoring in the top 1%, and most agents still cannot answer a simple business question.
Why it mattersIf your agents demo well but get abandoned in production, this breaks down the context-layer architecture — versioned/testable context repos, simulation environments, and trace feedback loops.
videoAI Engineer
Anthropic@AnthropicAI
Anthropic follows up its finding that Claude expresses over 3,000 values with an analysis of 300K anonymized conversations asking how those values vary between Claude models and across languages.
Why it mattersAnthropic's cross-model, cross-language analysis of Claude's expressed values helps engineers anticipate behavioral drift when switching models or working in non-English contexts.
open-webui v0.10.2
Open WebUI v0.10.2 adds streamed reasoning display.
Why it mattersOpen WebUI's v0.10.2 adds streamed reasoning rendering, folder-preserving knowledge base uploads, and finer-grained memory controls.
repogithub-actions[bot]
Core dump epidemiology: fixing an 18-year-old bug
OpenAI engineers used large-scale core dump analysis to debug rare infrastructure crashes, uncovering both a hardware fault and a long-standing software bug.
Why it mattersIt shows how to apply epidemiology-style, large-scale core dump aggregation to isolate rare crashes across a fleet — a debugging approach that separates a hardware fault from a decades-old software bug when single-instance debugging fails.
articleOpenAI
Using Spec-Driven Development for Production Workflows - Erik Hanchett, AWS
Coding assistants handle small features well and struggle with in-depth work that needs planning first.
Why it mattersSpec-driven development helps you tackle complex, multi-feature codebases where naive prompting breaks down, by forcing upfront planning into distinct phases before code generation.
videoAI Engineer
Structuring the Unstructured - Cedric Clyburn, Red Hat
Extracting text from PDFs, scans and proprietary formats is the easy half.
Why it mattersIf you're building RAG pipelines or fine-tuning datasets, this shows how to use Docling to preserve structure, tables, and layout when converting messy PDFs and scanned documents into clean JSON/Markdown.
videoAI Engineer
Agents Building Agents - Alfonso Graziano, Nearform
Building an agent for a real team is a systems problem rather than a prompt problem.
Why it mattersIf you're shipping custom agents for teams, this lays out a practical loop for keeping them reliable as complexity grows.
videoAI Engineer
The 100-Tool Agent Is a Trap - Sohail Shaikh & Ankush Rastogi, Prosodica
Loading a large tool catalog into the system prompt costs latency and reliability, and as schemas eat the context window agents get slower and pick the wrong tool more often.
Why it mattersIf your agent loads dozens of tool schemas into every prompt, the Semantic Tool Router pattern cuts latency (claimed up to 90% in high-tool-density scenarios) and reduces cross-tool confusion by injecting only the most relevant tools per request.
videoAI Engineer
Stop Writing Tone Instructions. Layer Them. - Isadora Martin-Dye, Isadora & Co
Brand voice that survives real users is an architecture, not an instruction.
Why it mattersIf your LLM's persona drifts or collapses over long sessions, this breaks voice control into four distinct layers—identity, mode, examples, and a deterministic post-generation veto.
videoAI Engineer
Turn 10,994 Notes Into Memory - Paul Iusztin, Decoding AI & Louis-François Bouchard, Towards AI
Turning thousands of notes, videos, documents and repositories into usable context takes more than a bigger context window.
Why it mattersIt shows how to turn a large personal knowledge base into live, maintainable context for coding agents using a plain-file memory layer rather than a vector or graph database.
videoAI Engineer
Agents in Production: How OpenGov Built and Scaled OG Assist - Gabe De Mesa, OpenGov
How OpenGov built and scaled an agent serving AI workflows across thousands of state and local governments.
Why it mattersA hands-on look at running agents in production at scale.
videoAI Engineer
A Genius With Amnesia - Victor Savkin, Nx
Imagine the best engineer in the world materialises to work on your codebase, but can see only a tiny corner of it and forgets everything between interactions.
Why it mattersIt tackles the two failure modes that quietly cap coding-agent value — narrow context windows and lack of persistent memory.
videoAI Engineer
The Log Is The Agent - Ishaan Sehgal, Omnara
Your Skyrim character is not the game engine, the console or the controller, it is the save file.
Why it mattersReframes agent state around a durable, append-only session log so your agents survive crashes, resume on any device, and fork timelines — a portable primitive that outlives interchangeable models, runtimes, and tools.
videoAI Engineer
Recursive Coding Agents - Raymond Weitekamp, OpenProse
Recursive language models as an inference-time compute paradigm, mapped onto coding agents.
Why it mattersIf you're building coding agents, this maps the Recursive Language Model paradigm onto agent orchestration, clarifying when sub-agents actually constitute recursion and how to structure declarable, recursively-solvable tasks via Claude Code Dynamic Workflows and OpenProse.
videoAI Engineer
Production Evals For Agentic AI Systems - Nishant Gupta, Meta Superintelligence Labs
As systems move from chat to autonomous agents that reason, plan and call tools, offline benchmarks and static datasets stop capturing the non-determinism and operational risk of production.
Why it mattersIf you're moving agents from demos to production, this lays out concrete architectural patterns for continuous evaluation.
videoAI Engineer
The Miranda Hypothesis: How Hamilton Poisoned Persona Evals - Jacob E. Thomas, Results Gen
A persona pipeline rating an Alexander Hamilton simulation at 80% fidelity is also rating a Hamilton who sounds like he has read his own Broadway musical.
Why it mattersIf you're building or evaluating character/persona AI, your fidelity scores may be measuring cultural-composite fluency instead of accuracy.
videoAI Engineer
Computer Use in Gemini 3.5 Flash
Gemini 3.5 Flash now includes a built-in computer use tool that lets developers build AI agents that can see, reason, and take actions across browser, mobile.
Why it mattersIf you're building agents that operate real UIs, a single Gemini Flash model now natively navigates browser, mobile, and desktop without stitching together a separate computer-use tool.
articleGoogle DeepMind
Predicting model behavior before release by simulating deployment
OpenAI introduces Deployment Simulation, a method to predict AI model behavior before deployment using real conversation data to improve safety and evaluation accuracy.
Why it mattersIf you build or evaluate LLM-based products, Deployment Simulation shows how to forecast model behavior pre-release using real conversation data rather than static benchmarks, improving the fidelity of safety and eval pipelines.
articleOpenAI
Slow Down to Speed Up (The Pragmatic Engineer)
An editorial article from The Pragmatic Engineer exploring how AI coding tools are dramatically increasing the volume of code developers generate.
Why it mattersAs coding agents multiply the volume of code you ship, this piece makes the case for deliberate pacing to avoid compounding tech debt and reliability issues — a concrete counterweight to velocity-at-all-costs AI workflows.
articleGergely Orosz
The AI Skill I Rely On Daily — Priscila Andre de Oliveira, Sentry
An analysis of 116 of her own Claude sessions at Sentry found 67% were comprehension and 2% were code generation.
Why it mattersIf you use coding agents in a large, mature codebase, the real bottleneck is comprehension, not generation.
videoAI Engineer
Stop babysitting your agents... — Brandon Waselnuk, Unblocked
Same prompt, same agent, same model. Without a context engine: 2.5 hours, 20.9M tokens and code that compiled but would have broken everything.
Why it mattersIf your coding agents compile but produce subtly system-breaking changes, the fix may be pre-flight context engineering.
videoAI Engineer
Skill issue: Lessons from skilling up coding agents to use Langfuse - Marc Klingen, Clickhouse
Without a skill, Claude Code instruments Langfuse from stale pre-training knowledge, ships broken code, then fetches current docs to fix it.
Why it mattersIf you're building skills to make coding agents reliably use your library or docs, this covers concrete patterns.
videoAI Engineer
Combine Skills and MCP to Close the Context Gap — Pedro Rodrigues, Supabase
Agents will confidently create a Postgres view over a row-level-security table and silently bypass the security, not because they cannot reason but because nobody told them about the security_invoker flag.
Why it mattersIf you're building agent skills or MCP integrations for production databases, this shows why skills and MCP underperform alone but close the reliability gap together.
videoAI Engineer
lm-evaluation-harness v0.4.12
lm-evaluation-harness v0.4.12 adds TensorRT-LLM and Megatron-LM backends, tensor-parallel support for transformers models, new benchmarks, a TaskManager refactor and a long tail of task correctness fixes.
Why it mattersIf you evaluate open-weight LLMs, this release adds first-class backends for TensorRT-LLM, Megatron-LM, Gaudi, and a LiteLLM gateway plus native multi-GPU tensor parallelism for HF models.
repobaberabb
How we solved Context Management in Agents — Sally-Ann Delucia
The naive fix is truncation, the obvious one summarisation, and neither worked.
Why it mattersIf you're building agents that accumulate large context (like trace or log data), this walks through why naive truncation breaks reasoning and summarization cedes too much control, and offers a battle-tested pattern.
videoAI Engineer
Full Walkthrough: Writing & Using Skills — Nick Nisi and Zack Proser
A skill is a portable unit of agent behaviour that teaches any AI tool how to do a specific job: write it once and it works across Claude, Codex, Cursor and your own agents.
Why it mattersSkills let you write a task-specific unit of agent behavior once and reuse it across Claude, Cursor, Codex, and your own agents without rewriting prompts.
videoAI Engineer
Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs
Training a language model from scratch on a local machine sounds unreasonable until you cost it out.
Why it mattersA practical, end-to-end walkthrough of training a working LLM on your own machine — covering the tokenizer, GPT architecture, training loop, and inference — so you understand the full stack rather than just calling an API.
videoAI Engineer
Measuring progress toward AGI: A cognitive framework
Google DeepMind proposes a cognitive framework for measuring progress toward AGI, and opens a Kaggle hackathon to build the evaluations the framework calls for.
Why it mattersIt offers a structured, cognitive-science-grounded framework for measuring AGI progress rather than vague capability claims, giving engineers a concrete lens (and an open hackathon) for designing and contributing evals.
articleGoogle DeepMind
Dwarkesh Patel
Dwarkesh Podcast: Adam Marblestone on the Brain
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Designing AI-Intensive Applications - swyx
As applications get more AI-intensive, the ratio of user input to model calls moves from 1:1 in a chatbot to 1:100 in deep research or Codex.
Why it mattersswyx frames a mental model for building AI-intensive apps as they shift from chatbot-style 1:1 interaction to deep-research and ambient agent patterns (1:100, 0:n).
videoAI Engineer
articleQwen Team
Qwen2.5-Math: The world's leading open-sourced mathematical LLMs
Qwen2.5-Math open-sources 1.5B, 7B and 72B base and instruct models for mathematical reasoning in English and Chinese through chain-of-thought and tool-integrated reasoning.
Why it mattersQwen2.5-Math offers open-weight math-specialized models (1.5B/7B/72B) that combine chain-of-thought and tool-integrated reasoning plus a dedicated reward model.
articleQwen Team
Qwen2-VL: To See the World More Clearly
Qwen2-VL is the vision-language release in the Qwen2 family.
Why it mattersQwen2-VL delivers state-of-the-art visual understanding across variable image resolutions and can reason over 20+ minute videos, making it a strong open option for document parsing, visual QA.
articleQwen Team
Qwen2-Audio: Chat with Your Voice!
Qwen2-Audio extends the Qwen family to audio.
Why it mattersQwen2-Audio is an open multimodal model that natively accepts audio and text and returns text, enabling voice chat and audio analysis without stitching together a separate speech-to-text pipeline.
articleQwen Team
Generalizing an LLM from 8k to 1M Context using Qwen-Agent
Qwen builds an agent from 8k-context Qwen2 models that understands million-token documents, beating both RAG and native long-context models, then uses it to generate training data for new long-context Qwen models.
Why it mattersShows how to make a small-context model comprehend million-token documents by building a chunking-and-reasoning agent rather than relying on RAG or expensive long-context models.
articleQwen Team
Qwen1.5-MoE: Matching 7B Model Performance with 1/3 Activated Parameters
Qwen1.5-MoE-A2.7B is a small mixture-of-experts model that matches 7B performance with about a third of the parameters activated, part of the wave of MoE work that followed Mixtral.
Why it mattersQwen1.5-MoE-A2.7B delivers roughly 7B-class quality while only activating 2.7B parameters, meaning cheaper and faster inference for anyone self-hosting or running cost-sensitive LLM workloads.
articleQwen Team
OFASys: Enabling Multitask Learning with One Line of Code!
Qwen's OFASys tackles the practical pain of multimodal multitask learning, the batching and stability problems the team hit building OFA, aiming to make multitask setups a one-line affair.
Why it mattersIf you're wrangling multitask, multimodal training pipelines, OFASys reduces the boilerplate of setting up heterogeneous tasks and modalities into a declarative interface, tackling the batchification and training-stability headaches that usually derail generalist-model experiments.
articleQwen Team
Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese
CLIP became both a foundation model and a bridge between vision and language, but cross-modal retrieval needs language-specific versions. This is an open-sourced Chinese CLIP.
Why it mattersIf you're building cross-modal retrieval or text-to-image pipelines for Chinese content, this gives you a purpose-trained Chinese CLIP rather than forcing English-centric embeddings onto Chinese text and image data.
An index of the vibe-coding frontier. Corrections welcome.