@nichycodes

🇮🇹 28 | 💼 Software Development Engineer

Milano, Lombardia
Joined December 2018
Nichy retweeted
🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4
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video made with HTML 1-shot presenting what's possible to do with @HyperFrames_ Studio, the final video editor that your agents need, prompt in the comments if you want similar output with Opus 5.5!
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Nichy retweeted
what a week for open weights! we got: - Bonsai 2 2B/4B/9B - Nemotron 3.5 Bolt 74B + Super VL 120B - MiniCPM-V 4.7 1B + 35B-A3B - LFM2.5-24B-A2B - Muse Spark Open 816B-A42B - Step 5 Preview 600B-A27B - Beam 1.0 501B A23B - Jais 3 family - Granite Switch 4.1 - Hy-Memory 30B-A3B - FLUX 3 Klein - MOSS-Image and next week Nemotron 3.5 Bolt E4B lands. my 3060 is not sleeping! configs/benchmarks for the ones i ran are below 👇
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Introducing OpenSource Free WebAccess - 100% FREE Web search & fetch @Tiny_Fish - Any web page & data, agent can access - Routing each request to the vendor best at that task - Benchmark on cost/speed/token-efficiency Try it at treg.to/web-access Repo link below 👇
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Small decision models can be insanely good when fine-tuned. We played with d1-omni-600M and it can destroy any model on the games + format (state, input) you train it on.
how do you fine-tune a decision model on your own data? in these two notebooks, you'll learn: • how to prepare the training data • how to fine-tune our d1-3B decision model • using @UnslothAI and @huggingface TRL all within 30 minutes on a free-tier GPU. unsloth: github.com/Liquid4All/cookbo… TRL: github.com/Liquid4All/cookbo… ps: how adorable are these halloween kitties? 👻
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Nichy retweeted
The web is free for humans. Agents shouldn't have to pay to read it. Our free Search and Fetch (30 searches a minute, no card) now power the first call in @treg_ai 's open source WebAccess. Try it: treg.to/web-access
Introducing OpenSource Free WebAccess - 100% FREE Web search & fetch @Tiny_Fish - Any web page & data, agent can access - Routing each request to the vendor best at that task - Benchmark on cost/speed/token-efficiency Try it at treg.to/web-access Repo link below 👇
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Nichy retweeted
Bonsai 2 4B Full Review 1GB VRAM, seriously it takes 1GB VRAM to run this. Out of the 3 new weights, this one is by far the most impressive, bench scores are 80-90% of Qwen3.8 27B in coding and general agentic. It starts to struggle for more complex, multi-step agentics and prone to over-thinking like all models in Qwen/Bonsai family tree. I recommend thinking on with minimum 8K budget, with thinking capped at 4K to greatly reduce this and improve overall performance. Runs very fast, full speed chart included below. Absolutely worth giving it a shot to see how it performs for your specific use cases, and once again, it’s ONE GB.
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This week, we launched Indra, a 1M parameter OCR model that's 15x faster than Tesseract on CPU and much more accurate. I still can't believe how good this model is.
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DeepSeek v4.1 Flash can now create stunning motion videos on par with Opus 5.5—with OpenDesign Motion. We asked OpenDesign Motion to make its own launch video. Here's the result. Demo 2 ↘️
Introducing OpenDesign Motion. The open-source alternative to Claude Motion. Create beautiful motion videos with any agent, any model. No Opus 5.5 required. This entire launch video was made in OpenDesign. More demos below ↓
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Nichy retweeted
Do you have 2 to 4 GB of vram? Great! Now you can run a lil guy
Today, we’re releasing Bonsai 2 2B, 4B, and 9B. Based on Qwen3.5, these new ternary models bring stronger reasoning, visual understanding, and tool calling to compact footprints designed for everyday devices: • 2B: 0.43GB • 4B: 0.95GB • 9B: 2.0GB For this release, we focused on what makes small models useful as local assistants: following instructions, avoiding repetitive loops, calling tools on mobile devices, and giving useful answers without unnecessary output. The 9B model uses roughly 40% fewer tokens on standard instruction-following and everyday math tasks. Solving the Riemann hypothesis can wait. These models are successors to our first-generation Bonsai 1.7B, 4B, and 8B, and provide a foundation for bringing practical agentic functionality to local applications on personal computers, phones, and wearables. The Bonsai 2 family now includes four sizes spanning 2B to 27B parameters. Developers can choose the balance of capability, memory use, and responsiveness that fits their application and hardware. Available today under Apache 2.0.
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A 3B active model beats Opus 5 at memory. We open-sourced the full stack — model, engine, and app. Hy Memory handles chat, work, and code memory in one layer, and it's first on all four benchmarks we ran, leading MemoryArena by 10.2 points while cutting memory volume 51.8% and recall tokens 39.3%. The model isn't tied to our engine. Drop Hy-Memory-30B-A3B into Mem0, MemOS, or EverMemOS and it still comes out on top, even on the one system it never trained on. It's already running in our own products, where one assistant's multi-hop recall went from 50% to 90%. It also unifies memory across Codex, Cursor, WorkBuddy, and more. Repo: github.com/Tencent-Hunyuan/H… Weights: huggingface.co/tencent/Hy-Me… Questions welcome in the replies.
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💥 A 9B AI model in just 2.4 GB? I put it to the test, hands-on! 🌿 Ternary Bonsai 2 9B from PrismML, built on Qwen3.5-9B, squeezes every weight down to just -1, 0 or +1 🔹 Runs locally with llama.cpp, with vision support 🔹 Image-to-code test with a doner kebab photo 🔹 Nadsat from A Clockwork Orange + a 28-language challenge 🔹 Wind tunnel chart reasoning + a comedy sketch from a photo 🔹 Verdict: sharp at seeing images, shakier on rare languages 🔥 Watch the full hands-on video below 👇 youtu.be/Y7mGNH4-zJI?si=BjX5… @PrismML
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Nichy retweeted
neuroplasticity can be improved by a single 30-min session of physical activity with noticeable changes after 15 min of exercising.
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Nichy retweeted
delete tiktok. watch old movies. embrace boredom. quit doomscrolling. eat without stimulation. hang out in book shops. slow your mornings down. walk without headphones. start believing in yourself. create phone boundaries. stare out the window. visualize your future. learn new skills. seek discomfort. experience life.
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JevImageBench V0.3.0 Yeah, I know @CompleteSkeptic, you are not the biggest fan of public benchmarks.... But maybe you appreciate taht Jev is #1 on the newest version of JevImageBench. Everyone, new version online, and the Top 5 has changed quite a bit: benchmarkheaven.com/image-je…
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It 's so amazing seeing iPhone screening call centers call with iOS 27.2 and people/bots on the other side giving up. Thanks @Apple for this amazing feature finally reaching Italy! Time saver!
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Introducing gemma.ai The new hub for Gemma, including 📚Inference and training guides 🧠 Deep visual architectural guides ‍🖼️ Online demos, even running models in the browser And a lot more to come, check it out!
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Welcome to the new Gemma documentation! We've built a brand new home for developers to explore, experiment, and build. What’s new? 🌐 Global model selector for customized code snippets 🤖 Better agent support (turn any page into Markdown!) 📊 Data-driven backend for a consistent experience ⚡️ On-device demos running directly in your browser! Check it out: gemma.ai/
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PrismML's Bonsai 2 small models are out -- 2B, 4B and 9B, real ternary, GGUF + MLX. (configs below for all three) a 9B in 2 GB, a 2B in 430 MB. with 32K context the 2B runs in under 1 GB of VRAM. that's on-device territory -- laptops, old cards, macs, fully offline. 9B on my RTX 3060 with PTQ1_0: ~114 tok/s decode, ~1,800 tok/s prefill, full 262K context + vision in ~7.5 GB. PTQ1_0 is the smallest file and on my 3060 it's also the fastest, ~15-20% faster decode than PQ2_0. needs PrismML's llama.cpp fork, stock llama.cpp won't load it. day one config, expect it to move. config: llama-server -m Ternary-Bonsai-2-9B-PTQ1_0.gguf --mmproj Ternary-Bonsai-2-9B-mmproj-Q8_0.gguf -ngl 999 -c 262144 -fa on --jinja -np 1 --cache-type-k q8_0 --cache-type-v q8_0 --temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0 --presence-penalty 1.0 --repeat-penalty 1.0 --reasoning on
Today, we’re releasing Bonsai 2 2B, 4B, and 9B. Based on Qwen3.5, these new ternary models bring stronger reasoning, visual understanding, and tool calling to compact footprints designed for everyday devices: • 2B: 0.43GB • 4B: 0.95GB • 9B: 2.0GB For this release, we focused on what makes small models useful as local assistants: following instructions, avoiding repetitive loops, calling tools on mobile devices, and giving useful answers without unnecessary output. The 9B model uses roughly 40% fewer tokens on standard instruction-following and everyday math tasks. Solving the Riemann hypothesis can wait. These models are successors to our first-generation Bonsai 1.7B, 4B, and 8B, and provide a foundation for bringing practical agentic functionality to local applications on personal computers, phones, and wearables. The Bonsai 2 family now includes four sizes spanning 2B to 27B parameters. Developers can choose the balance of capability, memory use, and responsiveness that fits their application and hardware. Available today under Apache 2.0.
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We made open models run over 2x faster than Ollama, without any new hardware Introducing Magnitude, an open source inference engine that runs open models as fast as your hardware allows. It tunes its kernels on your machine to find the fastest way to run each model Works on Apple Silicon, NVIDIA, AMD, or just a CPU On average across 11 popular local models: Mac M4 Pro vs Ollama (MLX) 1.32x faster prefill, 2.19x faster decode DGX Spark vs Ollama (llama.cpp) 1.34x faster prefill, 2.17x faster decode Ollama, LM Studio, and llama.cpp ship one-size-fits-all kernels, because tuning each model for each chip by hand would take forever Magnitude tunes them automatically. When you download a model, it spends up to a minute on your actual chip trying different ways to split up the math (how many GPU threads team up, how big a chunk each one takes, how far ahead to load data) and keeps the fastest combination that doesn't change the model's output Each model also comes paired with the best speculative decoding drafter, for even more speed One click connects Magnitude to the agent you already use (Pi, OpenCode, Hermes, Codex, and more). It's also built specifically for agents: • Each agent uses about half the memory, thanks to a quantized KV cache • Memory grows and shrinks with your sessions, so your computer stays usable • Agents share prefix caches, so several can run at once without slowing down Next, we're working on getting even more out of your hardware: • Expert streaming from RAM or disk, to run models bigger than your GPU allows • Splitting one model across all your GPUs and CPU so nothing sits idle • Attention pruning that skips low-impact math, for much faster prefill Ships as a desktop app on Mac, Windows, and Linux 100% free and open source Download → magnitude.dev/download GitHub → github.com/magnitudedev/magn…
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