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In Frontend Code Arena, Kimi K3 (Max) by @Kimi_Moonshot is ranked #1 among open and #1 overall! It’s the #1 open-weight model in all Domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Gaming, Simulations, and Content Creation Tools. And for overall, #1 in 5 of 7 Domains, landing #2 only in Gaming and Content Creation Tools.

parts I and II have over 700k views together. Now you get to read part III - benchmarking opus 5 on SlopCodeBench http://x.com/i/article/2081753730534506496

“The only job left is to understand what you don't like, put it into words, and tell the AI.” Here’s my new episode with @jxnlco (DevEx at OpenAI) where he showed me how he uses Codex throughout his workday, including how to: → Set up a chief of staff for Slack and email → Turn past sessions into new AI skills → Give long-running projects verifiable goals Some example Codex use cases from Jason: “I want you to read the past week of Slack messages I’ve written and make a skill to figure out how to talk like me.” “Turn this thread into a heartbeat. I want you to check my emails, my Slack, my Linear, and tell me what I need to prioritize.” “Codex can find a booking ticket and check into the flight for me and text me the boarding pass to my phone.” Jason wrote the official OpenAI handbook on how to get the most out of Codex, so don’t miss this episode. 📌 Watch now: https://youtu.be/ydTeb_I0b94 Thanks to our sponsors: @RiversidedotFM: All-in-one AI studio for podcasts and video https://creators.riverside.com/PeterYang Google AI Studio: Build apps with Nano Banana, and more https://aistudio.google.com/?utm_source=peteryang&utm_medium=partner&utm_campaign=26Q3-peteryang_ais

Piranesi city builder game

chatgpt work is remarkable, and "work" undersells it. from my phone i sent: "use all my chat history to figure out ideas for a long weekend trip with 8 friends, plan the best three options, make a full-stack site where the 9 of us can coordinate on what we would want to do in each place and decide where to go, and then after we get to group agreement make reservations. draft an email in my gmail i can send out to my friends when the site is ready." it...just worked.

Reading a book while having GPT-Live open to talk to, ask questions, write down notes and bookmarks really feels like reading in 4D

My biggest tip for Grok Build is to VOICE PROMPT. ctrl + space then speak. It is WAY faster than typing. AND Grok is very good at understanding what you want if you just start speaking in a rambling stream of consciousness that is indistinguishable from verbal diarrhea.

🎉 Congrats to the teams behind vLLM AFD Plugin (Ascend & vLLM, @StepFun_ai, @AntGroup, FastAFD): a new experimental plugin under vllm-project that brings Attention-FFN Disaggregation to MoE serving. Attention and the expert/FFN path are two very different workloads that normally share one topology. AFD runs them as separate services, so you can scale attention and experts independently. Same vLLM serving surface, no fork. @NVIDIA GPU and Ascend NPU.

Opus 5 crushed Fable 5 at 3D destruction physics for 2x cheaper! We gave four models the same task: build three self-contained HTML scenes with real physics Prompts: - A tornado that sucks in a whole field - A wrecking ball taking down an apartment block - An overloaded truck collapsing a truss bridge Outputs: - Opus 5: 55.9K tokens, $1.40 - Fable 5: 55.1K tokens, $2.82 - Kimi K3: 35.7K tokens, $0.55 - GPT 5.6: 20.1K tokens, $0.31 Opus got all three right unlike the other models. Houses fly up the funnel and out the top, the wall breaks where the ball hits and the rubble piles up, the bridge drops the truck into the river. Fable had almost nothing on the ground for the tornado to pick up, its building collapsed on its own before the ball even touched it and its bridge blew into sticks all at once. GPT is the cheapest here but its ball never reached the building at all and its bridge fell apart in a way nothing falls apart in real life. Kimi K3, the new Chinese frontier model, ended up in the same place as GPT

Grok 4.5 is #1 at processing real-world invoices At Ramp, we tested models on 150k bills submitted by actual businesses, scoring them on whether they predicted every correction a human would make Grok achieved the highest perfect-extraction rate, beating similarly priced models Gemini Flash 3.6, GPT 5.6 Terra, and Sonnet 5. This is a demanding long-context reasoning task. The model must infer patterns across 100K+ tokens of prior invoices, business memories, and human corrections, then apply them to new bills. The goal: zero-click accounts payable, with invoices processed correctly without human intervention.

claude opus 5 vs fable 5 vs gpt 5.6 sol vs kimi k3 @AnthropicAI released claude opus 5 today. per the announcement, it's "a thoughtful and proactive model that comes close to the frontier intelligence of claude fable 5 at half the price." key facts from the release: • priced at $5/mtok input, $25/mtok output – unchanged from opus 4.8 • anthropic reports it as state-of-the-art on frontier-bench v0.1 (43.3%), gdpval-aa v2 (1861), arc-agi-3 (30.2%), zapier automationbench (26%) and osworld 2.0 (70.6%) • per the same release, it stays behind mythos 5 on cybersecurity, and behind fable 5 on parts of agentic coding (deepswe 68.8 vs sol's 72.7, frontiercode 53.4 vs fable's 53.5) • runs at max thinking effort by default; a fast mode is offered at ~2.5x speed for 2x the price. our runs used the standard setting our test – 3 prompts, single-file html, @threejs, fully procedural, no assets. each has a small fire button (web audio gunshot + muzzle flash + recoil + ejected brass) and a disassemble toggle that explodes the gun into labelled parts and rebuilds it: 1. 5.56 m4 carbine – collapsible stock, safe·semi·burst lower, quad-rail with r14–r28 panel numbers, aimpoint red-dot, vertical foregrip, folding bipod, a2 flash hider, breaks into 10 parts 2. glock 18c – select-fire machine pistol, ported 18c compensator slots, "glock 18c / austria 9x19" roll-marks, extended 33-round mag, field-strips into slide, ported barrel, recoil spring, frame, mag 3. steyr tmp – ribbed polymer housing, threaded barrel, integral forward vertical foregrip, canted translucent 30-round mag ran on @aimlapi results: - cost #1 gpt 5.6 sol – $1.26 #2 kimi k3 – $3.60 #3 opus 5 – $6.22 #4 fable 5 – $8.12 - tokens #1 gpt 5.6 sol – 36,554 #2 fable 5 – 120,203 #3 opus 5 – 137,093 #4 kimi k3 – 196,596 - lines of code #1 opus 5 – 2,738 #2 gpt 5.6 sol – 2,432 #3 kimi k3 – 1,907 #4 fable 5 – 1,764 - generation time #1 gpt 5.6 sol – 7.8 min #2 fable 5 – 23.5 min #3 opus 5 – 24.8 min #4 kimi k3 – 93.8 min observations: • opus 5 wrote the most code and shipped the best-looking guns – the two go together. 2,738 lines, almost no boilerplate, and it beat fable at 3/4 of fable's price. all 3 models render, all 3 disassemble reliably • it is the only model that respected the black-on-black problem. its studio env is a narrow overhead strip with a comment explaining why ("narrow + long beats big + square here"), and parts separate by roughness, not colour – slide 0.44, ribs 0.52, grip 0.86. that is why the lighting reads like a product shot • the steyr ribbing and m4 proportions are the best in the test. the ribs are real half-buried cylinders merged into one geometry, each catching a single specular line – not a bump map. thin barrel, slim quad-rail, correct 84cm scale, all computed from named stations, not eyeballed • price per 100 shipped lines – gpt sol $0.052, kimi $0.189, opus $0.227, fable $0.460. opus buys frontier-grade detail for half of fable's per-line rate opus 5's code quality: upsides: - it engineers instead of eyeballing. everything is built to real-world scale (m4 = 84cm), the rail teeth sit at the exact 1cm pitch a real picatinny rail uses, and the steyr's ribbing is actual 3d geometry catching a specular line, not a bump texture faking it - it caught a classic three.js trap the others didn't – the bevel on extruded shapes quietly inflates the silhouette, so it pre-corrected every surface constant so decals and ribs sit flush instead of floating or z-fighting downside: - one systemic bug: every left-side engraving renders mirrored. roll-marks, the safe·semi·burst selector, the mag numbers – all reversed. single cause, but it hits all three guns opus 5 is the strongest engineer in this field – it saw traps the others walked into and reasoned about physics constants, real-world scale, and sequences instead of guessing follow @thehypedotnews for 24/7 ai news, analysis and breakdowns

WOW! I thought Opus 5 would just be a cheaper Fable. I was dead wrong. This model is absolutely BONKERS. I'm genuinely blown away. Opus 5 built the best Rocket League clone i've ever seen, and it did it while consuming only 27% of my 5x Max sub. Sound on. There's layers to this onion. I'm floored rn. At least for building games and 3D modeling, this has to be the best model in the world by a notable margin. Look at the car. The reflections. The details. Playable game & open source code in the comments.

On ARC-AGI-3, an evaluation where AI models must solve novel problems, Opus 5’s score is three times as high as the next best model.

Deepdive: Kimi K3 max vs. GPT 5.6 Sol max on software engineering/DeepSWE tasks. Both of these models provide nice specializations and routing/cascading between them is the right way to go! full deep-dive 👇(1/n)🧵

http://x.com/i/article/2079482473818333184