Your curated collection of saved posts and media
We gave robots eyes. Now we're giving them nerves. Introducing the world's most sensitive robot, this is unreal:
Introducing Silica. We make actuators 50x more sensitive at an affordable price, so that robots can feel the world around them. When I was at Apple, I developed technology that helped humans live more creative and connected lives. Now I make hardware for the training age, s
The best model depends on the job. A model that excels at coding may struggle with financial analysis. Models with similar headline scores can behave very differently when the assignment requires judgment, verification, or navigating dozens of messy files. That’s why we built DAYJOB and sudo L7 around work that professionals actually do. Today, six of those benchmarks joined @FireworksAI_HQ Specialized Intelligence Index, giving teams another way to compare models against the work they need done. Read more about what we test and how the benchmarks are built: https://t.co/Evtuox2Sbz
Introducing the Document Parsing Protocol (DPP) There are hundreds of different OCR models and tools, but they might all parse a document in different ways. Some errors are very clear-cut (e.g. hallucinating text, or completely misaligning a table), but many errors are much more ambiguous (e.g. how to represent split tables sharing a header). It's an issue that plagues most parse benchmarks too, because they inherently reward a certain style even if other styles might be semantically correct. We introduce a protocol that proposes a standard way of parsing these documents into a correct format. We've curated a list of 140 rules that cover literally every aspect of doc parsing, for instance: - tables: borderless whitespace tables, header-only tables, horizontal slicing, side-by-side mini tables - forms: key-value regions, checkboxes, checklists, ids and labels, fields outside the form, - charts: standardized chart output, multiple charts, gantt charts, line charts You can use DPP as an evaluation rubric for LLMs when determining the quality of parsing a document. It's not a full evaluation benchmark (yet), but an initial evaluation guide, and meant to encourage a discussion on what are the "best" ways to parse a document. Check it out: https://t.co/HsLh6ma9bB Blog: https://t.co/0md2O2M2Mf
Is that a table, or just text that happens to line up? If a table has two header rows, should the output preserve both? Parsers make these judgment calls differently. Those differences shape what your agent understands about a document. We built the Document Parsing Protocol (D
Particle is a new company focused on optimizing open-weight models to be more token-efficient and cost-efficient to serve. It's been great working together with Suhail and his team ahead of their launch! We look forward to working with them on tools like search and model orchestration.
1/ Excited to announce our new company Particle This is our first step in a series of models focused on developers. With particle-1, we focused on token efficiency and managed to keep parity in performance while using ~45% fewer tokens. Live now: https://t.co/m5MvOwR9yE https:
🚀 VS Code Wrapped: GitHub Universe 2026 A year ago, coding with agents was new. Now, you can run multiple agents in parallel, pick the right model for the task, and keep work moving across devices and the cloud, all while staying in control. ⚡️ Explore what’s new in VS Code: https://t.co/9bJlxxzNZY
Congrats to the @GoogleDeepMind team on the top new scores on Chartography and Riemann-bench!
Gemini 4 Carbon is our next frontier model. It’s state-of-the-art across cybersecurity and long-horizon capabilities.
Excited to share what we’ve been building: Carbon is here! Hands down the smartest model I’ve worked with, already my daily work partner, and casually helped me snag hard-to-get tickets🤣🥳 Enjoyed the process of building it to tackle open frontier problems. Also so proud of what our team did for Gemini's fast iteration. Let’s keep pushing! 🫡
I did say we'd be releasing our models periodically...introducing Gemini 4 Carbon, our next frontier model. Following Argon's release just two weeks ago, Carbon shows the pace at which we’re pushing the frontier, it ranks #1 on the recently updated v5.0 of @ArtificialAnlys Intel
Could this be a sign of the g factor in models? That is a test that could be done (factor analyze benchmark scores).
Plotting ARC-AGI model scores reveals a strong relationship across all three benchmarks. For example, the power curve v2 = v1^4.53 explains about 98% of the variation in ARC-AGI-2 scores. Models tied on ARC-AGI-1 can still differ by over 20 percentage points on ARC-AGI-2. https:
Copilot Automations ⚙️ Every developer has work they shouldn’t have to repeat. Define the job once, then run it on demand, on a schedule, or in response to activity in GitHub or a connected service. Share automations with your team and set their access, actions, and spending limits. https://t.co/9K9PC0t6yS
2/ We trained and pushed particle-1 to be more token efficient. Completing tasks end-to-end is noticeably faster too. We stand on the shoulders of giants and the model is based on DeepSeek V4.1 Flash 1017. We wrote up the details here: https://t.co/zKi6WN2YC9 One of our major goals is to reach continuous post-training: a new way to ship improvements to models as fast as we ship software. This is just our first step to hillclimbing towards efficient intelligence.
Yet again, AI development is moving faster than even the most aggressive predictions. With 3 days left in October, Google tied the human baseline on Blueprint-Bench 2. https://t.co/vH9XHDEhOR
Timelines just got shorter... AI will understand the physical 3D space better than humans by November* https://t.co/yRhjtVIMYM
Read the blog to learn more about Gemini 4 Carbon: https://t.co/xDOCBApGS2 https://t.co/eu3yNZZnje
Announcing Gemini 4 Carbon, our strongest model with state-of-the-art cybersecurity and long horizon capabilities. Carbon represents a step up across most benchmarks, delivering strong frontier performance in autonomous coding and cyber defense. We’re first making Carbon available to a subset of trusted cyber defenders in our Fairwind program. We’ll expand access as soon as possible.
Modular has made the full MAX source available, including the compiler, runtime and hardware backends. Developers can write once, run across accelerators and build commercial products on MAX without licensing fees or royalties. A big step toward making heterogeneous computing more accessible across CPUs, GPUs and accelerators. https://t.co/YpVRIRW4yV
incredible work. im in awe of anthropic’s pace, and am grateful for the transparency of process+results i went through 3 out of the 5 case studies they released and wrote down my thoughts on each. in italics are LLM-summarizations for the cases for those who havent read them https://t.co/bLJoqExbjc
llama.cpp b11938 on M2 Pro 32 GiB (using ~3 GiB with the full 128K context) at ~110 t/s with c1 running with Pi! llama serve --hf-repo LiquidAI/LFM3-VL-2B-GGUF:Q4_K_M --host 0.0.0.0 --port 8080 -fa on -np 1 --top-p 1.0 --min-p 0.0 https://t.co/enITzfYaPQ
incredible work. im in awe of anthropic’s pace, and am grateful for the transparency of process+results i went through 3 out of the 5 case studies they released and wrote down my thoughts on each. in italics are LLM-summarizations for the cases for those who havent read them https://t.co/bLJoqExbjc
Over the past few months, Anthropic scientists have engaged in deep research partnerships with six organizations developing new medicines. We've also begun drug discovery efforts of our own. In doing so, we’ve learned much more about how Claude can support the many stages of dru
Just finished reading the LLM Inference Handbook by @Modular. Excellent, beginner-friendly resource, even if you're unfamiliar with transformer architecture. Next up: @wafer_ai's GPU Performance Engineering resources.
From today, I'm starting my inference engineering journey by reading this handbook from @Modular . I have little to no idea about my roadmap but I'll just start learning and see how it'll go. I read "Inference Engineering" book by @philipkiely in the past so I have some knowledge
Recent weeks have made it clear that AI excels at solving research problems. How good is it at proposing *interesting* research problems? Thanks to the ratings of 29 experts across math, stats, CS, physics, we found 1/3 were interesting, and we fully expect this to improve. This raises important questions about the desired relationship between {humans,AI} in {academia,industry}...
AI is now taking on some of the hardest open problems that scientists have posed. But research is broader than solving problems. Can AI also propose research questions that experts find genuinely interesting? We built After42 to find out. Experts judged 45 of its 135 final quest
In agent driven startups, you can scale with vendors like Replit. I patched up 40+ apps < 2 hours! There is a page to view statuses of all of the vibe coded apps & prototypes. Where you can kickoff security patches and fixes and then deploy! https://t.co/7aFZeF5Bhw