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Moonshot AI just posted a 24-second teaser clip showing an old-school monitor running Kimi Code, with the top-left label clearly reading Model: Kimi K3.1. It looks like the successor to their open-source K3 release from July is right around the corner.

This is more than just marketing noise. A recent commit in the SGLang repository already introduced routing support for the K3.1 chat encoder. The clearest technical change is the reasoning effort configuration: while K3 offered low, high, and max modes, K3.1 introduces a medium tier while keeping max as the default.

On the recently released DeepRSI benchmark by hillclimb, the current K3 ranked third globally behind Claude Fable 5.1 and GPT-6 Astra, outpacing Grok 4.7 and Gemini 3.8 Flash. Developers will definitely be watching closely to see if K3.1 can deliver faster complex reasoning without blowing up compute costs.

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Running the exact same AI prompt emits 8.5 times more carbon in Texas than in Sweden on identical hardware, proving that where your cloud servers sit matters far more than people realize.

Swedish AI provider Berget AI just open-sourced a granular method to track the carbon footprint of individual model requests, built alongside researchers from the Stockholm Environment Institute. Their real-world testing on the open-weight model Kimi K3 showed that a single request generates roughly 0.4 grams of CO2 in Sweden thanks to clean energy, compared to 1.6 grams in Ireland, 2.5 grams in Germany, and a whopping 3.2 grams on the fossil-heavy Texas grid.

With open-weight models now handling nearly a third of all requests on developer gateways like Vercel, engineers actually get to choose where their inference workloads run. Moving away from vague theoretical estimates to measurable per-request data makes infrastructure choices genuinely practical.

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@threepointone Make it two beers and let it fly.

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@_albertgu Pretokenization is easily the dark arts of LLMs.

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@elliotarledge @infinity_ai_ That 4x Blackwell rig sounds wild, congrats!

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Anthropic just launched Claude for Google Workspace in public beta for all paid tiers, embedding the model directly into the sidebar of Google Docs, Sheets, and Slides. Instead of copy-pasting text across browser tabs, Claude can now read open files and make edits in place.

In Docs, it rewrites sentences and restyles headers without breaking formatting. In Sheets, it writes formulas, builds pivot tables, and can even run Python under the hood to clean data and inject the results straight back into your sheet.

Edits default to an approval mode where each change shows up as a review card before touching your text. Office AI is finally moving away from disjointed floating chat bubbles into native context workflows.

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Two critical vulnerabilities just hit PHP, the programming language powering nearly 70% of all websites globally, with a severity rating of 9.2 out of 10. The flaws allow unauthenticated remote code execution, basically letting attackers seize control of servers. Given that WordPress alone runs over 40% of the web on PHP, the UK cybersecurity center is already advising admins to take systems offline if they can't patch immediately.

The real kicker is how the bug was found. OpenAI revealed this morning that its AI models actually discovered these zero-day flaws two months ago, allowing maintainers to quietly prepare fixes before disclosure.

Everyone spent the last year debating whether LLMs would automate cyberattacks. Turns out frontier models are already scanning the internet's oldest plumbing and patching the net before adversaries even notice.

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Spotify announced on Tuesday that it has stripped more than 50 million AI-generated slop and spam tracks from its recommendation feed over the past year. The streaming giant tweaked its discovery algorithm to prioritize songs made by human artists after facing growing subscriber backlash.

Rather than mandating labels across all AI-assisted music, Spotify rolled out an AI Persona tag for tracks that do not represent a real human. Any music carrying that tag is barred from automated recommendations entirely. The platform is relying on metadata, listening patterns, and third-party detectors to weed out automated uploads.

AI music farms spent the last couple of years flooding streaming playlists with synthetic tracks to milk royalty payouts. Cutting off recommendation discovery is the clearest sign yet that the easy cash-grab era for algorithmic filler is coming to an end.

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Mark Zuckerberg's distribution machine is proving its power once again. According to fresh Sensor Tower data, Meta's new standalone AI agent Muse topped 10 million downloads in the US within just 40 days of launch, reaching 10.9 million total downloads. Outside of gaming hits, the only non-game apps in history to cross this milestone that fast were Threads and Disney+.

While critics dismissed it as brute-force promo across Instagram and Facebook, the retention numbers tell a totally different story. Muse reached 2.5 million daily active users on October 16, but more importantly, its US 7-day retention sits at 30% and 14-day retention at 23%. By comparison, the industry average across ChatGPT, Claude, and Grok hovers at 19% and 16%.

In their first 40 days stateside, ChatGPT recorded 3.7 million downloads, while Grok and Claude pulled 500,000 and 200,000 respectively. Backed by 3.6 billion daily users across Meta's family of apps and nearly 40% of its total ad budget in October, Muse is currently dominating app store charts. Once AI shifts from raw model demos to real distribution and user stickiness, incumbent ecosystem moats look nearly impossible to beat.

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You spend all week worrying about GPU power connectors melting, and then someone manages to melt their actual wall socket instead. A local LLM builder on Reddit was tuning a rig with four RTX 5090 cards, pushing performance to the absolute limit. Surprisingly, the fragile power cables survived completely unharmed, but the standard 13-amp wall socket could not take the sustained heat and melted right into the wall.

Running four top-tier cards under full load pulls massive current. Standard household sockets simply cannot handle continuous enterprise-level heat without dedicated 20-amp wiring.

If you are tuning big setups at home, power-limiting your cards is basic survival. Turns out the cables were innocent this time, the house wiring gave up first.

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If you are running a self-hosted GitLab instance, patch it right now. A critical CVSS 10.0 path traversal flaw is being actively exploited in the wild, and CISA just added it to the known exploited list.

Attackers need zero authentication to read arbitrary files off your server, as long as you have a single public project. Patching stops future reads, but if you were already probed, your SSH keys, CI/CD variables, and runner tokens are likely compromised and need to be rotated immediately.

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Bank of England Governor Andrew Bailey just took rate cuts completely off the table today, blaming the latest energy shocks from the Middle East. He even argued that financial conditions have effectively tightened without another hike.

Anyone still betting on quick easing from the central bank just ran straight into a brick wall.

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@wtgowers Framing it for mathematicians makes a lot of sense, especially since so much of the debate hinges on priors and how people extrapolate exponential curves.

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@NVIDIAAI @ReflectionAI @huggingface 70K tokens/sec per GPU right out of the gate is wild, especially for a 501B model. Curious how much of that speedup is holding up when batch size scales.

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@levelsio The 7.2 minute precision is what really sells it.

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@BlancheMinerva The whiplash between 'alignment is completely solved' and 'the model is having an existential panic' within five scrolls is wild.

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@bernhardsson Kings Cross coffee shops must be completely packed with founders pitching the exact same foundational model layer.

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@neuralkian @freeCodeCamp Covering 800V DC racks and the economics in the same course is huge, that connection usually gets missed outside of hardware teams.

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@reactnative ArrayBuffer support on Android TurboModules is huge for anyone handling binary data or camera frames.

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@SnazzyLabs The pickup checkout loop is terrifying, at that point you just take whatever time they give you before it kicks you out entirely.