Introducing LinkedIn for your Grok @bot. Your bot can now: > Read your inbox and tell you who's waiting > Draft replies from a quick note > Welcome new connections > Send connection requests with a personal note You approve every message before it sends. Pay-per-call. No subscription

The /goal-cup judges: @obra @motdotla @guzmanpintos @opencolin @wschenk @p3ob7o @batuhan, Dennis Groves of OWASP, and @boardyai as AI guest judge. They read your prompt, not only your product. agentcommunity.org/goalcup

hosted my first podcast. topic: why your Playwright script keeps chasing its tail 🎙️🐶

We’ve raised $1.3M from YC & Afore Capital so you never have to type or speak again. Typing is slow. Talking into your computer is awkward. Subvocal fixes both Our glasses give you the speed of speech without you making a sound or touching a keyboard. It tracks your internal speech muscles with non-contact sensing and decodes silent, almost invisible mouthing into text at more than 200 words/min. Pre-orders are now live at: subvocal.co/preorder?utm_sou…

Akash changed the course of my career. I learned so much about cloud hosting, documentation, CLI tools, ephemeral sandboxes, storage and eventually GPUs

"Compute markets are converging." – Nick Hansen, CEO The same miners, power sites, and capital that served Bitcoin mining are now powering AI. Here's where our $16M round led by @CMT_Digital fits in.

483 days ago, @5aitec and I raised $20M from @binnybansal to build ShopOS Millions of brand images and videos later we learned: the model was never the hard part. Getting it right the 10,000th time is Today, we’re launching @sloosh_ai to give every creator that secret sauce 👇

back in may when we did our first edition of agents & bagels, i ate the leftovers alone. fast forward to october our 16th edition, we ran out of bagels 😄 SF @Techweek_ edition of @scalekitinc's signature agents & bagels with @nebiusai @tavilyai @tenki 🥯

gm, don't blink or you'll miss it 250 sandboxes (500 vCPU and 1 TiB of RAM) provisioned in ~1s and destroyed in ~500ms tenki.cloud is built for elastic compute. Workloads can grab burst CPU on demand, at any scale Perfect for AI agents, RL rollouts, evals, and CI: anything that goes from idle to all cores in seconds

ngl TECHCRUNCH was pretty nice. Got my steps in, captured leads, high fived friends, skipped the parties and sold some AI code reviews for @tenki by @luxor

Tenki AI Code Reviewer is now on @cursor_ai Origin by @SpaceXSI! Every PR gets a summary, inline findings and a check. Install it from the Origin marketplace or the Tenki dashboard.

It was an absolute honor to organize and host The New Business Stack: Blockchain × AI alongside @luke152 and @Team1USA_ during SF @Techweek_! 🔺 We brought together founders, builders, and industry leaders to explore real-world business applications of blockchain and AI. A huge thank you to our incredible speakers and the projects they represent: • @luke152 | @Team1USA_ • @opencolin | @tenki & @AIsaOneHQ • @MyCrypt0world | Zulu AI Systems • @cryptomastery_ | @1clawAI • @ChiZhangData | @GoKiteAI • @eFortunapl | @blockseeAI • @eddiewang | @tenki & @luxor And thank you to every attendee, collaborator, and person who came out to support, connect, and contribute to the conversation. Grateful to everyone who helped bring this event to life! Check out our recap video below 👇

Our talk at @aiDotEngineer World's Fair SF is now live! 🎥 @dylan and I explored "Optimizing Open Models for Production" - the techniques that make LLM inference faster, more efficient, and cheaper at @nebiustf. We covered a few key optimizations: - KV caching - Reuse previously computed key-value states instead of recomputing them. One of the biggest performance wins, with 5-10x speedups in some scenarios. - Cache-aware routing - Route requests to workers that already have the relevant KV cache, improving cache hit rates and reducing redundant computation. - Speculative decoding - A smaller model drafts tokens while a larger model verifies them, accelerating generation without sacrificing output quality. - Disaggregated prefill and decoding - Prefill is compute-heavy, while decoding is memory-bandwidth-heavy. Running them on separate GPU pools lets us optimize each independently. - Quantization - Smaller models, less memory, faster inference. But aggressive quantization can hurt quality, so finding the right balance is key. Lots of practical techniques for getting more performance out of open models! 🎥 Watch the talk: piped.video/watch?v=TRe1u7dH… 📊 Slides: sujee.dev/events/2026-06-29_…

new compute is going live at tenki.cloud, so we're retiring our slower machines. every runner will run on fast hardware at the normal rate. no more premium pricing.

Saturdays are for racking and stacking more compute nodes for tenki.cloud, these high density microclouds are to increase our capacity on our CI runners

come say hi @ tenki.cloud sf office

Zup and Tenki on a rooftop. Good crowd, good view, that's the whole update. #Zup #Tenki

Monday's first stop for Tenki at SF Tech Week: Zero Day with @DG_Incubation & @OpenNetwork_Lab. Seed VCs, off the record, on what's actually fundable in physical AI and security. Then live demo pitches. partiful.com/e/V3dzumUhXBJNj… Looking forward to it!

We made your CI sticky! Sticky disks mean faster CI because the cache is already attached when the Jon starts.