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xAI has just released a STRONG model for vibe coding Grok Code Fast 1 is by far the fastest I've tested 🔥 → Usable in Cline (100% open source) → Also Cursor, Copilot, etc. → 160 toks/s (GPT-5 runs at 50 tok/s) → Free for the launch, 6x cheaper...

144,713 просмотров • 11 месяцев назад •via X (Twitter)

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GPT-5 is live in Cline. We've been working with OpenAI to get this model ready, and here's our take: it's disciplined, persistent, & highly competent. It's collaborative in planning & and a diligent operator while acting. It plans thoroughly, asks optioned follow-ups when needed, & then gets out of the way and ships code. On long tasks it keeps going before pausing to check in. It follows instructions to the letter. And most importantly -- it writes good code. GPT-5 is like "The Wolf" from Pulp Fiction. Comes in, assesses the situation, then executes. Here's what you can expect from GPT-5 in Cline: > verbose while planning; terse while executing > asks a lot of good clarification questions, & frequently provides options when appropriate > strong context retention and persistence over long horizons (256k context window) > good at diff-style edits and multi-file changes (we'll monitor as more usage data comes in) > quiet in Act mode -- writes code without yapping Metaprompting is another strength. We tested early with OpenAI and used GPT-5 to tune our own prompt for GPT-5. Here's a pattern we like: “Answer from your own perspective: what changes or additions would help you better follow this prompt? Here is the prompt (or snippet): [snippet]. Users have complained about X and Y. What minimal edits would you make while keeping the rest intact?” Do you need to change any of your existing patterns in Cline? No -- it's good out of the box. Give a clear goal and constraints, let it plan, then let it cook. Expect more clarifying questions than most models. Pricing: $1.25/M input tokens (+90% cache), $10/M output. Roughly half of Sonnet 4 ($3/$15). Want to try GPT-5? Use it in Cline today for pure, unfiltered inference via the OpenAI, Cline, or OpenRouter providers. (fyi -- GPT-5 one-shotted this browser DAW below on the prompt "build something impressive to show me what you're capable of")

Cline

63,496 просмотров • 11 месяцев назад

GPT-5.6 vs GPT-5.5 on my custom spaceship prompt. I gave both models the exact same custom prompt. This is also the same prompt I previously gave to Fable 5. For context, GPT-5.6 Pro worked for 87 minutes, while GPT-5.5 Extra High worked for 34 minutes and 42 seconds. As I’ve said before, based on great authority GPT-5.6 will be an incremental/soldi improvement over GPT-5.5, not a “Fable killer.” My rough expectation has been that it would trade blows with Fable 5 on some benchmarks, maybe win around half depending on the category, but not clearly surpass it overall. And again fable five will have bigger model smell, but this was expected. After testing this coding output, that view feels pretty accurate. GPT-5.6 is clearly better than GPT-5.5 in several visual areas. The lighting, shading, chairs, object details, and exterior of the spaceship looked noticeably stronger. The scene was also easier to test. I do want to give GPT-5.5 credit though. It built out the rooms much much better and the planets looked better than GPT-5.6’s. It was also interesting that both GPT-5.5 and GPT-5.6 produced better-looking planets than Fable 5 in this specific test. The downside with GPT-5.5 was stability. The game was much glitchier and harder to test compared to GPT-5.6. But when it comes to the core of the demo, which is the spaceship itself, Fable 5 still beat both models pretty comfortably. GPT-5.6 is impressive, but from this test, it looks exactly like what I expected which was a meaningful incremental improvement over GPT-5.5, at least for indie game demos, but not something that replaces Fable 5. In collaboration with Chetaslua

Chris

249,713 просмотров • 1 месяц назад

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.

Harman

41,148 просмотров • 16 дней назад