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Today we’re releasing Ramp SWE-Bench: a private, production-grounded coding benchmark created from real engineering problems we've faced at Ramp.

Ramp Labs

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188,015 görüntüleme • 2 ay önce •via X (Twitter)

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Alibaba just released a coding model that hits 82 percent on SWE-Bench Verified. That is the highest score ever published for an open-source model. The weights are free. The license is Apache 2.0. You can run it today. The model is Qwen 4 Coder 32B. Here is what 82 percent on SWE-Bench Verified actually means. SWE-Bench Verified tests whether an AI can autonomously resolve real bugs pulled from real production GitHub repositories. Not synthetic exercises. Real open-source projects that real teams depend on. A model gets a bug report, reads the code, writes a fix, and either passes the test suite or it does not. At 82 percent, Qwen 4 Coder 32B resolves 82 out of every 100 real production bugs it is given. Without a human guiding it. On code it has never seen before. For comparison: Qwen 4 Coder 32B: 82 percent SWE-Bench Verified. Open source. Apache 2.0. Claude Fable 5: 80.3 percent SWE-Bench Pro. $10 input / $50 output per million tokens. Currently suspended. GPT-5.6 Sol: Competitive on Terminal-Bench. $5 input / $30 output per million tokens. An open-weight model that you can download and run for free just beat both of them on the benchmark designed to measure real software engineering capability. Here is the architecture. Qwen 4 Coder 32B is a 32 billion parameter dense model. Not a Mixture-of-Experts. Every parameter is active on every request. This matters for inference: a dense 32B model runs on 22 gigabytes of VRAM, which fits on a single high-end consumer GPU or a MacBook Pro with 64GB of unified memory. The smaller variant, Qwen 4 Coder 4B, runs at approximately 135 tokens per second on an M5 Max and fits inside 8 gigabytes of RAM. For a model with usable coding capability, that is a new bar for what fits in a single laptop. The training methodology continued Alibaba's approach of reinforcement learning on verifiable coding tasks. The model gets rewarded when its code passes tests. It gets penalized when it fails. Over millions of training steps, the model learns to write code that actually runs rather than code that looks plausible. License: Apache 2.0. Full commercial use. No attribution requirement. No revenue threshold. No monthly active user ceiling. Weights: Hugging Face, available today. Runs on: vLLM, Ollama, SGLang, and any standard GGUF-compatible inference engine. Qwen 4 32B also runs at approximately 135 tokens per second on an M5 Max chip, setting a new bar for what a sub-8GB model can do on Apple Silicon. The open-source coding model just beat the best closed-source model in the world on the benchmark designed to test whether AI can actually do software engineering. The weights are free. The subscription is optional. Source: Autom8Labs AI Insight July 2026, State of Open Source LLMs June 2026, Kunal Ganglani blog June 2026.

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Looking forward to meeting with #NATO Defence Ministers. Our top priority is to boost defence spending & ramp up defence production. We are at a pivotal moment for our security. Make no mistake, NATO is strong today & we will become even stronger. #DefMin

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Over 1 billion PDFs are created every day, but your agents still can’t read them reliably. Today we’re releasing Parse 2.0, the most accurate document parsing API in the world. Extend already processes millions of pages daily for leading AI teams like Brex, Mercury, Opendoor, Flatiron Health, and hundreds of others. Now, its even better. Parse 2.0 is SOTA quality on RealDoc-Bench, our open source benchmark that measures agent success rate on real world docs that agents actually encounter in production. We trained Parse 2.0 on 1M+ pages of the hardest documents seen in production. Here’s how it stacks up: - #1 in healthcare, real estate, logistics, and financial services - 95.7% agent Q&A accuracy on 581 docs (next best: 92%) - 0.847 F1 on layout (next best: 0.759) Give it a try today and build production-ready document agents with Extend.

Kushal Byatnal

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Today we announced first U3O8 production from our 85%-owned Kayelekera #Uranium Mine in Malawi. This major milestone is a key final step in Kayelekera’s successful processing plant commissioning, with production planned to ramp up to 2.4M lbs of U3O8 per annum in Q1 of 2026. Hear Lotus' $LOT Managing Director Greg Bittar discuss in the investor webinar held earlier today – watch it here:

Today we announced first U3O8 production from our 85%-owned Kayelekera #Uranium Mine in Malawi. This major milestone is a key final step in Kayelekera’s successful processing plant commissioning, with production planned to ramp up to 2.4M lbs of U3O8 per annum in Q1 of 2026. Hear Lotus' $LOT Managing Director Greg Bittar discuss in the investor webinar held earlier today – watch it here:

Lotus Resources

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MiniMax M2.1 is officially live🚀 Built for real-world coding and AI-native organizations — from vibe builds to serious workflows. A SOTA 10B-activated OSS coding & agent model, scoring 72.5% on SWE-multilingual and 88.6% on our newly open-sourced VIBE-bench, exceeding leading closed-source models like Gemini 3 Pro and Claude 4.5 Sonnet. The most powerful OSS model for the agentic era is here.

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Former Congresswoman Marjorie Taylor Greene🇺🇸

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Fresh off the heels of being granted a temporary restraining order in the legal dispute with Angstrom Automotive, it looks like Cybertruck production has made a bit of a rebound today at the Giga Texas outbound lot! Many (perhaps 100 or more) were parked all over the lot today, some intermixed with Model Y’s and a few Cybercabs, others in groups by themselves, more autonomously driving to the EOL facility for processing and many at the Direct Delivery center. It’s great to see this & I hope signals a new ramp of Cybertruck production!

Fresh off the heels of being granted a temporary restraining order in the legal dispute with Angstrom Automotive, it looks like Cybertruck production has made a bit of a rebound today at the Giga Texas outbound lot! Many (perhaps 100 or more) were parked all over the lot today, some intermixed with Model Y’s and a few Cybercabs, others in groups by themselves, more autonomously driving to the EOL facility for processing and many at the Direct Delivery center. It’s great to see this & I hope signals a new ramp of Cybertruck production!

Joe Tegtmeyer 🚀 🤠🛸😎

26,896 görüntüleme • 8 gün önce

Hoping your coding agents could understand you and adapt to your preferences? Meet TOM-SWE, our new framework for coding agents that don’t just write code, but model the user's mind persistently (ranging from general preferences to small details) arxiv: ❓Motivation: Most coding agents today can plan, edit, run, and test code. But they still fail at a key part of real-world development, understanding the user! Underspecified, shifting, or context-dependent instructions can easily break them. You must have those moments when coding agents were running for 10 minutes and ended up producing things largely misaligned. (1/)

Hoping your coding agents could understand you and adapt to your preferences? Meet TOM-SWE, our new framework for coding agents that don’t just write code, but model the user's mind persistently (ranging from general preferences to small details) arxiv: ❓Motivation: Most coding agents today can plan, edit, run, and test code. But they still fail at a key part of real-world development, understanding the user! Underspecified, shifting, or context-dependent instructions can easily break them. You must have those moments when coding agents were running for 10 minutes and ended up producing things largely misaligned. (1/)

Xuhui Zhou

40,191 görüntüleme • 9 ay önce