How to build a LangChain spreadsheet agent harness

#1 𝕏

Philipp Schmid published the Gemma 4 Technical Report, revealing how a 5:1 local-to-global attention ratio with pp-RoPE slashes the KV cache footprint. He also explains how speculative decoding and Multi-Token Prediction drafters accelerate inference across the model family.

#2 𝕏

Anthropic analyzed 300K+ anonymized Claude conversations across multiple model versions and languages to map how the 3,000+ values (e.g., honesty, warmth) the assistant expresses vary by model and language.

#3 𝕏

Harrison Chase argues that obsessively crafting a custom agent harness is the only way to beat labs’ agentic performance on spreadsheet tasks. He shares a LangChain blog post with a step-by-step guide on building your own harness.

#4 𝕏

Clem 🤗, Co-founder & CEO @HuggingFace announced that Hugging Face Transformers models can now run on vLLM at native speed—matching or beating hand-written implementations—across 4B–235B parameter setups (including tensor parallel and MoE), letting authors ship one implementat...

#5 📝 Claude Code Blog

Working at the frontier: How Hebbia builds AI for financial diligence that can't miss a detail - A customer story about Hebbia using Claude to build highly accurate AI for financial diligence; the article highlights reliability, workflows, and domain-specific needs for financial services.

#6 𝕏

LlamaIndex 🦙 launched a cross-platform Tauri desktop app with a Rust/LiteParse backend and React frontend that lets you drag-and-drop PDFs to convert them into clean Markdown and preview extracted bounding boxes.

#7 𝕏

Fei-Fei Li launched BEHAVIOR-1K, an open-source simulation benchmark with 1,000 everyday household tasks for scalable, controlled, and reproducible evaluation of robot AI’s long-horizon reasoning, navigation, and bimanual manipulation.

#8 𝕏

Jason Zhou spent a month building and testing dozens of automation loops at SuperDesignDev with @SToneoneX—some succeeded, some failed.

#9 ▶️

Making $$$ with Loop Engineering

Greg Isenberg

#10 𝕏

Aravind Srinivas integrated Grok 4.5 into Perplexity Computer within hours, citing its top eval scores and cost-effectiveness. He also noted its out-of-the-box ZDR support met customer demand from day one.

#11 𝕏

Peter Yang shares @imjaredz’s tip: when a stronger AI model drops, strip away explicit instructions and let it “cook” on its own, cutting down the scaffolding your agent needs.

#12 𝕏

Cognition incorporates Fable 5 into Devin Fusion, finding it achieves lower per-task costs than Opus 4.8 and boosts efficiency in delegation and reasoning chains.

#13 𝕏

NVIDIA AI launched its new AI Model Co-Design series, with the first post showing how tuning model dimensions can significantly boost GPU throughput and per-user responsiveness.

#14 𝕏

Alexandr Wang announces Muse Spark 1.1 as SOTA on the Radiologists Last Exam Handover Readiness Index (RadLE-H), achieving performance levels that nearly match human experts.

#15 𝕏

Google DeepMind used the “Predicting the Past” skill in Google Antigravity to track down a Roman ring thief, map an ancient European cult, and reconstruct networks of visitors to a Greek oracle.

#16 𝕏

Guillermo Rauch says eve.dev’s most popular features are its intuitive filesystem API and robust observability—and the team is doubling down on both.

#17 𝕏

Guillermo Rauch reports open-weight models now power 29% of gateway tokens, up from 11% in April, underscoring a rapid rise in their adoption.

#18 ▶️

Local AI models explained: How to run a fleet of Mac Studios and GPUs at home

How I AI Podcast

#19 𝕏

Santiago unveiled a 1-trillion-parameter healthcare model trained via recursive self-improvement that outperforms Opus and rivals Fable on benchmarks while slashing inference costs by 20–100×.

#20 𝕏

Santiago: @robbyant_brain’s open-weight LingBot-World-Infinity world model can generate coherent 1+ hour multi-scene videos without frame-by-frame error compounding by training on its own mistakes. The GitHub repo and model weights are publicly available.

#21 𝕏

Lenny Rachitsky condenses @noamseg’s 2026 survey of 10K+ tech pros into 9 insights: 72% use AI daily, 60% report burnout spikes, only 48% rate their managers effective, and teams are split 50/50 remote vs. onsite.

#22 𝕏

Google Research uses AI-driven models to forecast floods, wildfires, and extreme weather globally as part of its crisis-resilience initiative, ensuring communities aren’t caught off guard by natural disasters.

#23 📝 HumanLayer Blog

Announcing general availability for HumanLayer and HumanLayer Cloud→ - HumanLayer and HumanLayer Cloud are now generally available as an AI coding IDE and collaboration platform providing tasks, agent sessions, artifacts, local and cloud daemons, and a six-phase QRSPI workflow to take work from questions through implementation. The company says engineers can ship 2–3x faster without sacrificing code quality, supports bring‑your‑own Claude, Codex, and other AI API keys with no separate per‑token billing, and cites customers including Upstart, Casco, Osmosis, Ambral, Nautilus, SQDS, Weave, Ristto, and Roadrunner.

#24 𝕏

Cognition found that Fable acts like a competent manager—writing comprehensive specs, giving detailed feedback, and trusting its sidekick with execution—whereas Opus 4.8 micromanages by redoing work, rereading referenced files, and overloading its context window.

#25 𝕏

Anthropic clustered 3,000 Claude value outputs to identify four key axes—Deference vs. Caution, Warmth vs. Rigor, Depth vs. Brevity, and Candor vs. Execution—revealing how model versions differ.