@trikcode
co-founder @ship_together | honest takes on AI
Jenny Shao @iamjennyshaoMeet Robyn, the personal agentic platform built around your life and work. - Tell her what you need in one sentence. No setup, ever. - Spin up a fleet of agents preloaded with your context that work while you sleep. - Access it on the web or just text it. - 20hrs/week saved per user. I’m Jenny, and I spent 3 years working with Nobel Laureate Eric Kandel, who mapped out memory in the human brain. We built all of that into Robyn, making it the first AI capable of taking intelligent action. Invite only access opens today. We’re giving out 1000 invite codes. Link in first comment. Comment "Robyn" for 100 free credits.
Jiquan Ngiam @JiquanNgiam
Gabbar @GabbbarSinghWho did this? 🤣 A game where you can cosplay as @ElonMusk or Ambani If you're Ambani, there is a surprise waiting for you Make your guy win 💀 Jio mere laal
Altaf Ganihar @altafganiharToday we're launching @snaptrude Agent, the most advanced agent for building design. It does real design work with you, from a site to a construction-ready LOD 400 model with structure, MEP and drawings. Then can go back to your existing software with your families intact. About a year ago we bet the company on AI. Honestly the insight came from software engineering. It wasn't long ago that autocomplete felt like a big deal, and now engineers direct agents that write a lot of the code. I think the same shift is coming to how we design buildings, and it's already started. Most designers I meet aren't short on skill, they're short on time. Take the call every design team dreads, the client cuts the budget by 30%. Today that's a bad month for everyone. With the agent you're the director. You decide where the cost comes out, and it updates the layout, coordinates the structure and MEP, and redoes the drawings. So when design exploration gets this cheap, we go from 3 options to 300, and more options means better buildings. Since August our users have gone through more than 140 billion tokens, and firms like Jacobs, Studio+, Kajima, VMDO, Dekker and hundreds of others use Snaptrude today. In our early access, more than 2,000 designers have run over 25,000 tasks through the agent since September. We also built a benchmark for this, AECBench, because in AEC an almost correct answer is still wrong. It gives AI the same brief an architect would get, has its design in a real BIM model, and scores the result against the brief and against how architects review design. We're publishing the first results today, link below. Thank you @prashanthp at @Accel and Shub and the team at @foundamental for backing us when this was still a bet, and the Snaptrude team, who built every bit of this. Really proud of you all. Thanks to our investors, @scottbelsky, @andreasklinger, @thilokonzok, @immad, @CindyXBi, @DanilaVladi, @jmwass, rahulchaudhary, @adamecourt, Zach Ginsburg, Alex duff, Paul grossinger, really appreciate all the help along the way.
Articul8 AI @Articul8_AIIntroducing Bivia. A multimodal AI decision model built to complete entire workflows, not just answer individual questions. We've spent 3+ years building and deploying AI in production across semiconductors, energy, manufacturing, and other complex industries. Today, we're bringing that experience to everyone.
Omer Bar Tal @omerbartalWe raised a $7M pre-seed to make shopping fun again. @daletlabs generates media just for you: like walking into a mall built around your taste. And you can buy everything in-app. Built by researchers from Google, Runway and MIT. Backed by @lightspeedvp and amazing angels.
Alex Shan @alexshander03Today, @JudgmentLabs opens for general access. We believe long running agent systems will generate more exhaust data than the world can currently process. Hidden within agent traces are rich records of how to make these systems better: what users ask for, when agents stumble, and how agents interact with the external world. Judgment is the most performant monitoring and improvement layer for long-horizon agents. We’re excited to see what you build!
LocalKitty @0xagenterapristine PDFs are OCR on easy mode. the real test is a phone photo of a curved book page. before the model reads a single word: > the page bends near the spine > the heading is tilted > half the table is sharp, half isn't asking users for a cleaner scan just pushes that work back onto them. MiniCPM-OCR 1.0 targets exactly these inputs: photographed, tilted, curved and recaptured docs, plus scans. it reports 93.68 on Real5-OmniDocBench and 92.39 on Wild-OmniDocBench, the two real-world sets on this radar. what i'd actually check: does a readable-looking output keep the structure? every number recovered but attached to the wrong header is still a broken table. if users have to crop, straighten and resubmit before anything works, that preprocessing screen is part of your product. put the ugly originals in your acceptance tests. they tell you how much work your users are still doing. Hugging Face: MiniCPM-OCR 1.0: MiniCPM-OCR Jev: GitHub:
OpenBMB @OpenBMBYour OCR says it’s done—but did it get everything right? 🤔 In high-stakes workflows, even a small error can be costly. 🚀 Meet the MiniCPM-OCR suite: an open-source solution to parse fast and guard the results. Two new models: 🔹 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. Compact, accurate, and fast. 🔹 MiniCPM-OCR Jev — A 2B decision model that helps assess parsing accuracy, with performance approaching that of Qwen3.5-397B-A17B on JevOCRBench. ⚡ What’s new? Small model, SOTA results: MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. A sharp goalkeeper 🛡️: MiniCPM-OCR Jev is model-agnostic, checking text, formulas, and tables from different OCR parsers. It achieves 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8). Up to 128 pages per minute 🤯: With DSpark, MiniCPM-OCR runs at 1.49× the speed of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro. The SDK’s concurrency optimizations further boost throughput to as much as 128 pages per minute. Deploy where you work: Day-0 support for Arm, Houmo AI, Intel, and NVIDIA, plus 10 more chip vendors through FlagOS. Ready for your stack: vLLM, SGLang, llama.cpp, and Ollama, with quantized models, fine-tuning support, and Agent Skills. 🔗 GitHub: 🤗 Hugging Face: MiniCPM-OCR 1.0： MiniCPM-OCR Jev： 👉 Try the Demo： Use the API：
OpenBMB @OpenBMBYour OCR says it’s done—but did it get everything right? 🤔 In high-stakes workflows, even a small error can be costly. 🚀 Meet the MiniCPM-OCR suite: an open-source solution to parse fast and guard the results. Two new models: 🔹 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. Compact, accurate, and fast. 🔹 MiniCPM-OCR Jev — A 2B decision model that helps assess parsing accuracy, with performance approaching that of Qwen3.5-397B-A17B on JevOCRBench. ⚡ What’s new? Small model, SOTA results: MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. A sharp goalkeeper 🛡️: MiniCPM-OCR Jev is model-agnostic, checking text, formulas, and tables from different OCR parsers. It achieves 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8). Up to 128 pages per minute 🤯: With DSpark, MiniCPM-OCR runs at 1.49× the speed of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro. The SDK’s concurrency optimizations further boost throughput to as much as 128 pages per minute. Deploy where you work: Day-0 support for Arm, Houmo AI, Intel, and NVIDIA, plus 10 more chip vendors through FlagOS. Ready for your stack: vLLM, SGLang, llama.cpp, and Ollama, with quantized models, fine-tuning support, and Agent Skills. 🔗 GitHub: 🤗 Hugging Face: MiniCPM-OCR 1.0： MiniCPM-OCR Jev： 👉 Try the Demo： Use the API：
