Sabitlenmiş Tweet
VCs as rappers – no explainers:
Sequoia: Jay-Z
Greylock: Future
Felicis: Gunna
Amplify: Young Thug
Benchmark: Kendrick
Index: Skepta
Lux: Tyler the Creator
Tiger: Kanye
Khosla: Eminem
a16z: Drake
English
Tobi Coker
457 posts
Tobi Coker
@tecoker
venture @felicis SF based, ATL native free time // 📷: https://t.co/kcntgPWauQ
San Francisco, CA Katılım Nisan 2011
1.7K Takip Edilen1.1K Takipçiler
Congrats to the @StainlessAPI team on the acquisition by Anthropic!
It took six customer intros and two months to get @RattrayAlex to take a meeting with us — but from that first 25-minute conversation in a cramped NYC office, the vision for API infrastructure was obvious.
Excited to have partnered with Alex, @laurenmhreeder, @JenniferHli, @calvinfo, and team. Huge milestone.
cc: @felicis
anthropic.com/news/anthropic…
English
Tobi Coker retweetledi
We're thrilled to announce that Stainless is joining @AnthropicAI! Stainless was founded to make software better for everyone, and we're honored to continue that mission on the Anthropic team. Check out the announcement blog post for more: stainless.com/blog/stainless…
English
Tobi Coker retweetledi
We backed @StainlessAPI because @RattrayAlex believed SDKs deserved the same craft as the APIs they wrap. Anthropic believed it too. They've powered every official Claude SDK since day one. Congrats to the Stainless team on joining @AnthropicAI!
Anthropic@AnthropicAI
Anthropic is acquiring @stainlessapi, an SDK and MCP server platform that has powered every Anthropic SDK since the earliest days of our API. Read more: anthropic.com/news/anthropic…
English
Tobi Coker retweetledi
Anthropic is acquiring @stainlessapi, an SDK and MCP server platform that has powered every Anthropic SDK since the earliest days of our API.
Read more: anthropic.com/news/anthropic…
English
The @Felicis Forecast: the nine themes that we think will define what’s coming next. They don’t fit neatly into categories, but start to pop up everywhere once you notice them.
x.com/felicis/status…
Felicis@felicis
1/ Today we launch the @Felicis Forecast. It's our map of the changes that defy neat categorization but matter most to what’s next. Where AI’s potential cuts across industries to collide with consumer, organizational, and socio-economic fault lines. Where the old structures have ruptured, and new ones are just coming into view. Because even to people like us – generalists who’ve spent the past 20 years studying change – this moment feels different. Everything’s up for grabs. Rules, tidy bell curves, and comfortable patterns no longer apply. So instead of following patterns, we forecast: 9 themes we just can’t get out of our heads, that seem to pop up everywhere once you notice them. ordnl.link/Vyjrzep
English
The Royal Pop swatches have more character + individuality in a single plastic bead than all of you Royal Oak “watch guys” who were looking for any way to be different from a Rolex guy but not so different that you’d actually have to learn anything about watches send tweet
English
We’re launching @JudgmentLabs today and announcing $32M in funding.
As AI agents take on more of the work that creates economic value, they generate massive amounts of production data: the clearest record of how they behave with users, software, and the real world.
Judgment builds infrastructure for improving AI agents from production data.
English
AI is the first software wave to reach mass deployment before its infrastructure was built.
We surveyed 23 engineering leaders running AI in production. Three findings will shape how we invest in infra for the next 12 months — all consequences of the same pattern: the workloads got here before the stack.
Read more: felicis.com/blog/the-ai-st…
English
i just started the fastest company to $ 5M ARR.
sold a pair of airpods to my friend for 100 dollars,
transaction took ~10 minutes from procurement to close, meaning i've hit approximately a $5,256,000 annualized run rate.
taking only tier 1 investors in my dms
English
AI shipped to prod before its infra was built — first software wave that's done that.
We surveyed 23 eng leaders. 70% doubled inference spend in 6 months. Half ship agents as core product. Most monitor it on home-built Grafana.
Building primitives? Reach out.
felicis.com/blog/the-ai-st…
English
Had a great chat with Harry Kim from @nvidia , @philipkiely from @baseten , and Arya Asemanfar from @SierraPlatform, for a salon dinner on long-running agents and inference.
We were joined by 20+ technical leaders from @Replit , @nvidia , @hippocraticai , @PrimeIntellect , @anyscalecompute , @togethercompute, @thinkymachines, @netflix , @tryramp , @MemGPT and more.
Consistent themes that emerged through several conversations:
- Nobody owns the agent harness yet. App teams are building it by default, inference platforms want to absorb it from below. Sentiment pointed to the application layer as its home — though some platforms are getting close.
- Evals are the precondition for everything else — model right-sizing, routing decisions, fine-tuning confidence. Nothing off the shelf is specific or good enough to work generally. Everyone is handling it in-house.
- The next wave for inference is real-time: video, audio, multimodal. Beyond that, inference-time compute and observability are the areas people are focused on.
- VR as a longer-term area of interest, when the technology can serve the capabilities consumers actually need.
Super energizing to chop it up with some of the best in the business! cc @felicis
English
Tobi Coker retweetledi
Tobi Coker retweetledi
Pancreatic cancer mRNA vaccine shows lasting results in an early trial. Scientists caution that more research is needed, but nearly all of the patients who responded to the personalized vaccine are still alive six years later.
nbcnews.com/health/cancer/…
English
Tobi Coker retweetledi
We hosted Prof. Alyosha Efros (UC Berkeley) at @SkildAI! He didn't believe that robots could actually cook eggs reliably. :)
Tested back-to-back 5times without fail! One batch of scrambled eggs every ~2.5mins nonstop. The same model assembles a GPU on a server rack too.
English
Tobi Coker retweetledi
Grateful to be working with @schwarzjn_ and his team at Thomson Reuters to help leverage their proprietary data to mid-train the world's best legal models!
Mid-training on domain specific data can massively improve specialized performance without sacrificing general capabilities. And because the mid-trained model understands the domain better, post-training becomes far more effective.
Check out our case study linked below to learn more. And if you want to leverage your own proprietary data to build strong, domain specific models where accuracy and reliability are key, please reach out to us @datologyai!
DatologyAI@datologyai
We’re excited to announce our partnership with Thomson Reuters, a collaboration focused on unlocking the full potential of proprietary data to build the next generation of domain-specific AI. By applying DatologyAI’s data curation pipeline for legal domain adaptation mid-training, the results were clear: - +5% improvement on legal benchmarks and +2.5% on general-purpose evaluations after mid-training - >2.5x amplification in post-training gains on Thomson Reuters’ private legal evals - Achieved with <1% of the original pre-training token budget These gains demonstrate that better data doesn’t just improve models, but multiplies the effectiveness of everything built on top of them. As @schwarzjn_ , Head of AI Research at Thomson Reuters, put it: “DatologyAI delivered clear, measurable improvements across both public and our proprietary legal evaluations…demonstrating the strength and generalizability of their approach.” This partnership shows what’s possible when proprietary data and advanced data curation come together — not just incremental gains, but compounding advantages across the entire model lifecycle. We’re excited to continue building with Thomson Reuters to push the boundaries of domain AI. #AI #MachineLearning #LegalTech #DataCuration #Partnerships
English
spent the last few weeks in the weeds asking AI engineers & CTOs on how they're actually building in 2026 – inference spend, GPU infra, model strategy, async workloads for @felicis' first AI dev survey.
some of the results are... not what we expected 👀
who should we be talking to?
English