Stanford researchers have developed "Evo", a genomic language model trained on bacterial genomes. Unlike AlphaFold which predicts structure, Evo works at the DNA level, learning from gene clusters.
It has successfully generated functional novel proteins (CRISPR inhibitors, antitoxins) with <25% similarity to known sequences.
Generative AI is now effectively simulating evolution at the nucleic acid level.
https://arstechnica.com/science/2025/11/generative-ai-meets-the-genome/
Is it a waste to stop all roadmap work for a week just to fix bugs?
Lalit Maganti argues the opposite. His team of 40 engineers paused everything to fix 189 "papercut" bugs.
The result wasn't just a cleaner product. It was a team reconnecting with the joy of building. "Craftsmanship and care" often get lost in quarterly planning. Fixit weeks bring them back.
A great read on how to structure these weeks (and why they matter):
https://lalitm.com/fixits-are-good-for-the-soul/
Discussed on Hacker News, lobste.rs and r/programming It’s Friday at 4pm. I’ve just closed my 12th bug of the week. My brain is completely fried. And I’m staring at the bug leaderboard, genuinely sad that Monday means going back to regular work. Which is weird because I love regular work. But fixit weeks have a special place in my heart. What’s a fixit, you ask? Once a quarter or so, my org with ~45 software engineers stops all regular work for a week. That means no roadmap work, no design work, no meetings or standups. Instead, we fix the small things that have been annoying us and our users: an error message that’s been unclear for two years a weird glitch when the user scrolls and zooms at the same time a test which runs slower than it should, slowing down CI for everyone The rules are simple: 1) no bug should take over 2 days and 2) all work should focus on either small end-user bugs/features or developer productivity.
Google's AI infrastructure chief says they need to double capacity every 6 months to meet demand. The goal is 1000x in 4-5 years.
The most interesting part? They aim to do this at "essentially the same power and energy level" using custom silicon like their new TPUs.
Given the concerns about AI's energy footprint, this efficiency target is the most critical metric to watch.
#AI #Google #Sustainability #TechNews
https://www.cnbc.com/2025/11/21/google-must-double-ai-serving-capacity-every-6-months-to-meet-demand.html
If you are building AI developer tools, you know that "context" is the hard part.
kit is an open-source Python toolkit designed to solve exactly that. It provides robust tools for:
Codebase mapping & symbol extraction
AST-based code search
Chunking files for LLM context windows
It even ships with a local MCP server (kit-dev) to expose your repo's intelligence to MCP-compliant clients.
ChatGPT group chats have launched globally. You can now add AI to your group conversations (up to 20 people) to help plan, write, or debate.
Important for privacy: OpenAI states that personal settings and memories stay private to each user, even within the shared group context.
It's available for Free, Plus, and Pro users.
Read more:
https://techcrunch.com/2025/11/20/chatgpt-launches-group-chats-globally/
How to write effective `agents.md` files for GitHub Copilot. The shift from a generic "chat" interface to defined "personas" is significant.
The key takeaway from analyzing 2,500+ repos? **Context is King.**
- Give the agent executable commands.
- Define strict boundaries (what NOT to touch).
- Provide "gold standard" code examples.
For the CLI lovers out there: check out `hl` by pamburus. 🖥️
It's a modern, high-performance log viewer written in #Rust. It takes JSON or logfmt streams and renders them in a human-readable format with automatic color coding.
Key features:
- ⚡️ Extremely fast (handles multi-GB files easily)
- 🔍 Native filtering by level, time, and fields
- 🐧 Cross-platform (Linux, macOS, Windows, NixOS)
Sustainability at Solutrans wasn't marketing—it was operational constraint. Fuel costs, emissions regulations, urban access restrictions. Companies like Chéreau retrofit aerodynamic fairings not for CSR reports but because operators demand lower consumption. The most credible sustainability solutions treat it as engineering problem, not brand positioning. Results matter more than narratives.
#Sustainability #GreenLogistics #OperationalEfficiency #Transport #ClimateAction
Google continues its AI push with Nano Banana Pro, now running on the Gemini 3 Pro model announced earlier this week.
Interesting shift from the viral consumer use case (3D selfies) to enterprise utility (infographics from code/resumes). The ability to maintain character consistency across multiple generations suggests better control in the underlying model.
Full details here:
https://www.cnbc.com/2025/11/20/google-nano-banana-pro-gemini-3.html
Gemini 3's benchmarks are impressive (95% on AIME 2025, 76% on real coding tasks), but let's talk about what this means for the ecosystem.
Yes, it's proprietary. Yes, it's Google. But these performance leaps raise important questions: How do open models keep pace? What happens when proprietary AI becomes essential infrastructure?
https://deepmind.google/models/gemini/
#ArtificialIntelligence #OpenSource #TechEthics #MachineLearning
