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Open-source ecosystem for high-leverage AI builders. GitHub's #1 AI Agents Repo with 75k+ stars. Join 200k+ active AI builders.
Microsoft just released an AI agent to vibe with your data. Load data from screenshots or messy text and let AI agents explore and visualize it. Switch between agent mode and manual control. 100% free and open-source.
More such AI tools and projects in https://t.co/BvTc8nQQW5: Get access to 100+ AI Agent, RAG, LLM, and MCP tutorials with open-source code - All for FREE.
Now live on @acquiredotcom! You can now manage multiple and sell multiple startups from one account. Just click "manage startups" and then the "+" sign to create a new listing. Check it out and share any feedback!
Founder and CEO of @acquiredotcom. https://t.co/wRMIssDmhl has helped 100s of startups get acquired and facilitated $500m+ in closed deals.
I bet steam frames turn 2d games stereo. Should be doable, proven out by trioviz. Will be really cool
Opening portals to handheld VR at https://t.co/A2JMItorCV. Problems soluble, potential to improve invariant.
Since the embargo is now over, happy to share the slides of the first ever presentation of Baguettotron at @EPFL and a few additional thoughts beyond the blogpost. As many noticed we took an inspiration from the Physics of Language Model which coined the expression "synthetic playground" and promoted the use of synthetic data to design systematic "controlled experiments", bringing in effect LLM research closer to physics than to the current very empirical approach to data. The conference includes some very early controlled evaluations favoring deep layer architecture. We believe that depth scaling benefit the most from dense reasoning traces, likely allowing for a more optimal combinatory process across layers at inference time. A key topic of discussion has been the precocity of reasoning signals with SYNTH and that prompted me to retro-actively benchmark the 150 checkpoints of Baguettotron. I must say I was surprised by the overall results suggesting that the model is non-random in MMLU with a few billion tokens in (and @mkurman88 is now showing it might happen much earlier than that) I really hope to see this dataset powering more intriguing discoveries on LLM training over the next few months, as we have now the ability to run full training not just ablations with very few tokens and parameters.
Full presentation: https://t.co/3jPDpExmey
A real story from Reddit. Using Claude Code heavily for 6+ months: Why faster code generation hasn't improved our team velocity (and what we learned) https://t.co/cQRGOv44ve Main takeaway: AI shifted the bottleneck from WRITING code to UNDERSTANDING and REVIEWING it:
~20 yrs in web-dev, now mostly Laravel. My Laravel courses: https://t.co/HRUAJdMRZL My Youtube channel: https://t.co/qPQAkaov2F
