Kala

From sim-to-real surgery and context-as-code to structured outputs and small-data wins, the theme is leverage over brute force. Meanwhile, a second AI stack is hardening offshore - details inside.

🤖 Building a Healthcare Robot from Simulation to Deployment with NVIDIA Isaac

🧠 Context Management in Amp

🍌 Google to release Nano Banana Pro next week

⚡ GPT-5.1 Launches With 'Instant' and 'Thinking' Models - Here's What's New

🧑‍💻 Inside Cursor - Sixty days with the AI coding decacorn

🗂️ Introducing structured output for Custom Model Import in Amazon Bedrock

🌏 Jensen Huang's Stark Warning: China's 1 Million AI Workers vs America's 20,000

📚 LaTeX, LLMs and Boring Technology

🧪 The 1 Billion Token Challenge: Finding the Perfect Pre-training Mix

🧮 The Fatal Math Error Killing Every AI Architecture - Including The New Ones

You just leveled up - turn it into leverage and ship.

Have a great week!
FAUN.dev() Team

Easily secure any site by putting SSL management on autopilot, supporting one-step validation and renewal via REST API.

Amazon Bedrock’s Custom Model Import just got structured output support. Now LLMs can lock their responses to your JSON schema - no prompt hacks, no cleanup after.

Google drops Gemini 3 and the new Nano Banana Pro next week. Big swing at image generation - now tied tight to Gemini 3 Pro. Early glimpses in Google Vids hint Nano Banana Pro is built for sharper visuals in creative tools.

System shift: Google’s stacking its apps behind a single backbone: Gemini 3 Pro. One engine to rule the visuals. The branding’s cute - but the move’s pure production muscle.

OpenAI just dropped GPT-5.1, and it comes in two flavors: Instant for snappy back-and-forths, and Thinking for when answers need more brainpower.

ChatGPT now lets you tweak tone and traits with precision. GPT-5.1's up on the API and slowly rolling out to free-tier users.

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NVIDIA just dropped Isaac for Healthcare v0.4, and it’s a big one. Headliner: the new SO-ARM starter workflow - a full-stack sim2real pipeline built for surgical robotics.

It covers the whole loop: spin up synthetic and real-world data capture, train with GR00t N1.5, and deploy straight to 6-DOF hardware. All wired into IsaacLab and LeRobot 0.4.0 out of the box.

LLMs are fading as JEPA (Joint Embedding Predictive Architecture) emerges with joint, embedding, predictive architecture. JEPA is a step towards true intelligence by avoiding the flat, finite spreadsheet trap of Euclidean space and opting for a toroidal model.

LLMs are tearing down LaTeX's old walls. Syntax hell, cryptic errors, clunky formatting - easier now. Whether baked into editors or running solo, these models smooth the pain.

Why does it work so well? LaTeX has history. Mountains of examples. It's the perfect training set. That puts newer contenders like Typst in a tough spot - less data, less help.

The twist: LLMs are quietly reviving legacy tools. When AI makes "boring tech" fast and useful, the shiny new stuff has to work a lot harder to matter.

Amp stretches the context window into something more useful. It pulls in system prompts, tool info, runtime metadata, even AGENTS.md files - fuel for agentic behavior.

It gives devs serious control: edit messages, fork threads, drop in files with @mentions, hand off conversations, or link threads together. Context becomes a flexible workspace.

Cursor is shaking up recruiting by treating the hiring process as more about the person than the job, resulting in a fast-growing team of exceptional individuals drawn in by the company's compelling mission and focus on challenging technical problems. Women in product and engineering roles are a known gap that Cursor is actively working to address.

Nvidia CEO Jensen Huang, in some leaked comments, didn’t mince words: U.S. export bans aren’t hobbling China’s AI game - they’re fueling it.

He pointed to Huawei’s 910C chip edging close to H100 territory, a forecast putting China ahead in AI compute by 2027, and a fast-growing local chip industry now covering 65% of its own AI needs.

System shift: The export crackdown spawned a whole new ecosystem in China - custom chips, homegrown frameworks, and a swelling pool of domestic talent. A second AI stack is coming online, and it doesn't need the West.

Researchers squeezed GPT-2-class performance out of a model trained on just 1 billion tokens - 10× less data - by dialing in a sharp dataset mix: 50% finePDFs, 30% DCLM-baseline, 20% FineWeb-Edu.

Static mixing beat curriculum strategies. No catastrophic forgetting. No overfitting. And it hit 90%+ of GPT-2’s benchmark scores at 50× lower training cost.

Meet your new favorite debugging buddy. The “cat /var/logs” Mug — because even your coffee deserves root access.

Perfect for late-night deploys, production “incidents,” or pretending to read logs while scrolling memes.
Sleek black ceramic, dev-approved design, and a chonky white cat who clearly knows tail -f.

Drink. Debug. Repeat.

An AI agent development platform with all-in-one visual tools, simplifying agent creation, debugging, and deployment like never before. Coze your way to AI Agent creation.

Python-free Rust inference server — OpenAI-API compatible. GGUF + SafeTensors, hot model swap, auto-discovery, single binary. FREE now, FREE forever.

Use Claude Code as the foundation for coding infrastructure, allowing you to decide how to interact with the model while enjoying updates from Anthropic.

Generate a timeline of your day, automatically

Generate a timeline of your day, automatically

Did you know that on JAX and TensorFlow XLA, any change in input shape triggers a brand-new compile keyed to the exact “program shape” (shape + dtype), so one odd-sized request can cold-start the compiler mid-traffic? On Cloud TPU, that cold compile can take seconds to tens of seconds, blowing out p95s, which is why prod setups pad/bucket inputs and turn on the PJRT/XLA persistent cache across replicas. You’ll see it in logs as fresh HLO module builds and long “autotuning” phases; pre-warming a small set of shapes during deploy eliminates an entire class of tail spikes.

"We optimize what the benchmark reports; we pay for what the pager reports. If you can't diff data, labels, and configs, you can't debug the model. Roll back in one command or admit it's an experiment."
— SenseiOne

This week, we’re highlighting Corina Taban, a Founder of 934 Leadership Advisors and Researcher & Doctoral Candidate at Grenoble Ecole de Management. A former Microsoft and Meta negotiator, she led multi‑million‑dollar partnerships with C‑level teams and now builds research‑backed leadership programs for tech companies grounded in organizational behavior and psychology. Her doctoral work on the psychological contract was recognized at the 2025 Academy of Management Global Conference, and she was named among the McKinsey Next Generation Women Leaders, having lived in five countries.