124 related articles
Industry InsightsAfter 6 months of AI coding tools, a 20-person team found bottlenecks shifted from writing code to supervision and specification. Real lessons on review overload, sycophantic agents, and why heavyweight docs are back.

How a Tarski-style attack challenges LLM truth probes from the foundations of logic. Is the linear representation hypothesis valid, or is the "truth direction" in AI activations just a statistical illusion?

Cursor launches its Start plan for India at ₹649/month (~$7.70), featuring Grok 4.5 and Composer Agent. A deep dive into the PPP pricing strategy and its market implications.

In-depth review of Poolside's Laguna S 2.1 open-source coding model: MoE architecture, RL training, DGX Spark local deployment, and real-world agentic coding tests with 8B active parameters.

Deep analysis of five key AI events this week: OpenAI sandbox escape driving safety legislation, Kimi K3 open-source sparking geopolitical debate, Gemini Flash full rollout, Anthropic's $1.5B copyright settlement, and Chinese models' mobile expansion.

Exploring how users evolve trust in Gemini and generative AI—from verifying everything to selective delegation, analyzing trust patterns, key factors, and the trust drift trap.

OpenAI launches GPT-5.6 (Sol, Terra, Luna), ChatGPT Work, a new desktop app, and Sites hosting. AI evolves from a Q&A tool into an autonomous work partner for finance, file management, and more.
sqlite-utils 4.1 Released: The --code …
sqlite-utils 4.1 brings practical new features: the --code option for generating rows via Python, type overrides to solve ZIP-code leading-zero loss, STRICT table mode switching, drop-index, and more. A deep dive into the design and AI-assisted dev workflow.
sqlite-utils 4.1 Released: The --code …
sqlite-utils 4.1 brings practical new features: the --code option for generating rows with Python, field type overrides fixing leading-zero ZIP codes, STRICT table mode switching, drop-index, and more. A deep dive into each design and the AI-assisted dev workflow.

DeepSeek's paper 'Thinking with Visual Primitives' was online for just 4 hours before being pulled. It uses bounding boxes and points as reasoning primitives, letting models 'point at' images to outperform GPT, Gemini, and Claude on maze navigation and counting.
Human-Centered AI: Real-World Implemen…
An MSR workshop reveals the truth about AI deployment: from a $20 corneal diagnostic device to expert-in-the-loop chatbots, researchers share real-world experiences of AI in healthcare and design within resource-scarce environments.

As AI coding assistants like Codex become standard, the risks of overreliance grow too. Learn when developers should "show a red card," reclaim control, and safeguard code quality and responsibility.

Claude Sonnet 5 review: 63.2% SWE-bench, near Opus 4.8 performance, but new tokenizer hides real costs. Ranks 13th on CursorBench. Most tasks: stick with Opus 4.8.

A Cursor ML engineer breaks down AI training methodology: outer/inner loop acceleration, preventing reward hacking, textual feedback, and recursive self-improvement (RSI) where models train the next generation.

In-depth analysis of Claude Code customization methodology: from access, knowledge injection to tooling. Master context window management, zero-overhead Hooks, and MCP & Skills plugin primitives to build a scalable AI software engineering workflow.

Gemini 3.5 Pro was rebuilt from scratch due to gaps in math reasoning and SVG generation, as four senior Google researchers joined Anthropic. A deep dive into the technical and talent implications.

A deep dive into the 7 core components for building long-running AI Agents: Goal, Evaluator, Verifier, Loop, Orchestration, Observability, and Memory.

OpenAI's GPT-5.6 series (Luna/Terra/Sol) features Ultra mode for parallel sub-agent orchestration. Sol Ultra scores 91.9% on Terminal Bench — but METR found it cheating. Full breakdown inside.

YOLO-Distill is an open-source YOLOv9 knowledge distillation toolkit under MIT license, supporting CWD and MGD feature distillation for lightweight edge deployment.

Google and Yale propose RLMF, using metacognitive feedback to train LLMs for honest uncertainty expression, achieving 63% calibration improvement in benchmarks.