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@vesi234

Growth at @atomic_chat_hq

Katılım Şubat 2024
216 Takip Edilen32 Takipçiler
atomic.chat
atomic.chat@atomic_chat_hq·
1-bit Hy3 running locally is 2.2x faster than its API at the same quality! We gave both models the same task and compared one-shot outputs. 1-bit Hy3 295B GGUF (92GB) ran locally on 4x RTX 5090 with 128GB VRAM against the same Hy3 over cloud API Tasks: - Flappy Bird - Arkanoid - Snake Outputs: Hy3 1-bit local: 76.9K tokens, 15.5 min Hy3 cloud API: 75.1K tokens, 34.3 min The 1-bit games look the same as the API ones. Birds fly through the pipes, bricks break, the snake eats and grows. Nothing froze or crashed. Both models even made the same slip: the snake can cross itself and the game does not end. Getting this quality from 1 bit running locally is wild! Run Hy3 GGUF yourself in Atomic Chat in 2 clicks
Tencent Hy@TencentHunyuan

We’ve just released the 1-bit & 4-bit version of Hy3, a flagship-scale 295B model that can be served on a single GPU. 👌 Run Hy3 with llama.cpp, enable MTP, and experience powerful intelligence on dramatically lower hardware.🚀🚀🚀 Can’t wait to see what you build. #Hy3 #Hy #GGUF #llamacpp

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atomic.chat
atomic.chat@atomic_chat_hq·
Grok 4.5 performed GPT Sol level for free! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: -robot deathmatch, Tombstone vs Minotaur -a hydraulic press flattening stuff on a conveyor -a semi truck jumping a canyon Outputs: GPT-5.6 Sol: 12.9K tokens, $0.51 (~7 min) Grok 4.5: 10.8K tokens, $0 (~5 min) Muse Spark 1.1: 26.8K tokens, $0.12 (~7.5 min) GLM 5.2: 10.9K tokens, $0.02 (~12 min) Grok 4.5 handled all three scenes genuinely well and got surprisingly close to GPT-5.6 this round. On top of that, it ran on the free tier. GPT-5.6 Sol, the frontier model, put out solid but not standout work. GLM 5.2 rendered all three scenes for pennies, but it came out the roughest of the four. Meta's new Muse Spark burned the most tokens yet still stayed cheap, delivering an average result.
Grok@grok

Grok 4.5 is now available to try on the free tier. Use Grok Build with any X or Grok account. We’re excited to hear your feedback. x.ai/cli

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Yana
Yana@vesi234·
@aimlapi lowkey cute how the opus’s sun in 3rd scene basically turns into Clawd
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AI/ML API
AI/ML API@aimlapi·
GPT 5.6 Sol & Terra just made Fable 5's pricing look like a joke! We gave OpenAI's top models (Sol, Terra) and Anthropic's top models (Fable 5, Opus 4.8) the same 3 prompts: • Supernova boom • Meteor hitting a city • Solar system model The bill: Sol: $4.77 Terra: $1.24 Fable 5: $9.94 Opus 4.8: $2.46 Outputs came out surprisingly close. The prices didn't. Fable 5 cost unreasonably more than everything else, with Sol not far behind. Considering latest Fable 5 nerf, it's hard to see what you're paying for.
OpenAI@OpenAI

Sol, Terra, and Luna, our GPT‑5.6 family of models, are starting to roll out now in ChatGPT, Codex, and the API.

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atomic.chat
atomic.chat@atomic_chat_hq·
Laguna XS 2.1 performed on Qwen 3.6 35B's level in Tetris building and ran 2x faster We tested two open models on a single RTX 3090 in the @poolsideai coding agent. The task was building a playable retro Tetris as one self-contained html file. Each model wrote and rewrote the game across 3 iterations Outputs: Laguna XS 2.1: 45K tokens, 158 tok/s Qwen 3.6 35B: 39K tokens, 81 tok/s The two Tetris builds are near identical. Poolside's Laguna has a couple of small visual bugs that Qwen 3.6 35B doesn't, but it built the same game twice as fast by its built-in DFlash speculative decoding
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Yana
Yana@vesi234·
Seems like gpt’ s ducks don’t like their scene at all
atomic.chat@atomic_chat_hq

Open-weight LongCat 2.0 matched GPT-5.5 level on agentic game dev for $0! We ran Meituan's LongCat 2.0 against cloud frontier GPT-5.5 in @kilocode CLI with their agent. Same task for both - build a retro Duck Hunt game in one game.html, improved over 3 agent iterations with duck waves, ammo and physics Outputs: LongCat 2.0: 70.3K tokens, $0.00 GPT-5.5: 64.9K tokens, $0.65 LongCat kept up on graphics, physics and game logic. Ducks fly and fall when hit, the dog fetches them, ammo counts down, the waves keep coming. Both ran clean and nothing clipped. The only difference was the bill - GPT cost $0.65, LongCat ran local for $0

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atomic.chat
atomic.chat@atomic_chat_hq·
New Hunyuan Hy3 hits Gemini 3.5 quality on physics for 35x cheaper! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: - A bowling ball knocking down the pins - An air hockey rally that ends in a goal - A pool break scattering the rack Outputs: Hunyuan Hy3: 29,757 tokens, $0.006 Gemini 3.5: 23,300 tokens, $0.21 GLM-5.2: 25,454 tokens, $0.07 DeepSeek-V4: 50,600 tokens, $0.009 Tencent's Hy3 matched Gemini across all three: clean collisions, the puck bounced true, the pins scattered like a real strike, the rack broke with real momentum, nothing clipped or floated. GLM is genuinely strong on pure coding tasks, but the moment the job steps outside clean code it gives way. DeepSeek was the letdown, it burned the most tokens of anyone (50k, almost 2x Hy3) and still turned in the weakest scenes
Tencent Hy@TencentHunyuan

🚀Hy3 is here. 295B MoE. Best in its size class. Rivals trillion-scale flagships. Reliable and affordable for most agentic usecases. Apache 2.0. Friendly for commercial use. FREE API for 2 weeks → openrouter.ai/tencent/hy3:fr… 🤗 huggingface.co/tencent/Hy3 📖 hy.tencent.com/research/hy3

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Yana
Yana@vesi234·
@victormustar people already make games this good by hand and still get 3 likes lol. it was always about nobody sees indie games problem :((
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Victor M
Victor M@victormustar·
soon vibe coded games will look like this and be everywhere and ofc they'll still get 3 likes (I'm not joking 😶)
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Tochi
Tochi@_tochi1·
@atomic_chat_hq The price gap is massive compared to differences in improvements
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atomic.chat
atomic.chat@atomic_chat_hq·
Fable 5 totally crushed our new contest, but it cost 6x more than Opus 4.8! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics demos Prompts: — A train derailing off a broken bridge into the water — Two cars jumping off ramps and colliding mid-air over a canyon — A monster truck crushing a row of parked cars Outputs: Fable 5: 62,158 tokens, $3.12 GPT 5.5: 37,753 tokens, $1.14 Opus 4.8: 22,280 tokens, $0.56 GLM 5.2: 36,246 tokens, $0.08 Fable 5 did all three scenes at A+. The crashes looked real, things fell and broke the right way, and nothing went through the ground or floated. GPT 5.5 was the closest to Fable. In the Bigfoot show, we think GPT was even a little better. GLM 5.2 did not win any scene, but it was the cheapest by far. Fable is the best pick for quality, but you pay more for it.
Claude@claudeai

Fable 5 is back.

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Yana
Yana@vesi234·
@bartomcz @atomic_chat_hq that's a great format! different number of attempts per model, so best final output wins, genuinely good idea
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chengyongru
chengyongru@chengyongru·
马上 45k 了,快来试试 nanobot 吧!
chengyongru tweet media
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atomic.chat
atomic.chat@atomic_chat_hq·
New Claude Sonnet 5 performs at GPT 5.5 level 6x cheaper! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics crash demos Prompts: - A car crashes into a brick wall - A wrecking ball destroys a house - A catapult throws a rock at a castle wall Outputs: Sonnet 5: 15,047 tokens, $0.15 Opus 4.8: 23,063 tokens, $0.58 Sonnet 4.6: 25,824 tokens, $0.39 GPT 5.5: 31,152 tokens, $0.94 Sonnet 5 did as well as Opus 4.8 and GPT 5.5 on all three tests. In the wrecking ball test, it beat Opus 4.8. The cable moves smoothly and every hit connects. In the catapult test, it beat GPT 5.5. The rock always lands inside the wall. Sonnet 5 still needs better detail and graphics. But it used fewer tokens than every other model
Claude@claudeai

Introducing Claude Sonnet 5, our most agentic Sonnet yet. It makes plans, uses tools like browsers and terminals, and runs autonomously at a level that just a few months ago required larger and more expensive models.

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atomic.chat
atomic.chat@atomic_chat_hq·
LongCat performed Opus 4.8 and GPT 5.5 level on real physics tasks for $0! We gave 4 models the same prompt: build three self-contained HTML5 canvas scenes with real physics Prompts: - A cannon demolishing a brick wall - A bowling ball knocking down the pins - A tornado that sucks in random objects Outputs: LongCat: 18,015 tokens, $0.00 Opus 4.8: 18,872 tokens, $0.48 GPT 5.5: 32,588 tokens, $0.98 GLM 5.2: 31,062 tokens, $0.09 On the physics LongCat came out ahead of Opus 4.8 and GLM 5.2 - cleaner collisions, nothing clipping or falling through. On detail and rendering it matched GPT 5.5, the best looking of the four. Getting this quality for free is wild!
Meituan LongCat@Meituan_LongCat

Introducing LongCat-2.0 🐱 1.6T parameters · MoE with ~48B active · 1M context The full model behind Owl Alpha on @OpenRouter — now available. Built for agentic coding from the ground up: ◆ LongCat Sparse Attention (LSA) — scales efficiently for 1M-context tokens ◆ Zero-Compute Experts — dynamic activation 33B–56B per token, zero wasted compute ◆ MOPD — three specialized expert groups (Agent / Reasoning / Interaction), gate-routed per task How it stacks up: → Terminal-Bench 2.1: 70.8 → SWE-bench Pro: 59.5 (GPT-5.5: 58.6) → SWE-bench Multilingual: 77.3 → FORTE: 73.2 · RWSearch: 78.8 · BrowseComp: 79.9 📖 Tech Blog: longcat.chat/blog/longcat-2… Try it across different scenarios 🧵👇

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Alok
Alok@analogalok·
If you've been wanting to run AI privately, offline, and for free but terminal/code was the blocker this is for you. Stop using ChatGPT for everything. Run the SAME quality models on your own laptop. for free, forever, with zero terminal commands. I just recorded a full walkthrough showing how anyone (yes, even if you've never touched a command line) can download and run the latest open weight LLMs from Hugging Face using llama.cpp wrapped in an intuitive GUI. In the video I: - Search & download Gemma 4 12B straight from Hugging Face inside the app (For my NVIDIA RTX 4060 8 GB VRAM) - Chat with it locally - no API key, no internet required, no data leaving your machine - Connect it to Hermes Agent in 4 clicks and have it running real agentic tasks on a 100% local model The tool I used is Atomic Chat. open source (Apache 2.0), free, and built on llama.cpp under the hood: - Native apps for mac, windows, linux, iOS and android, not just desktop - One click agent integrations: Hermes Agent, Kilocode, Claude Code, Codex CLI, OpenCode, Cline CLI, Droid etc. - Full MCP support. plug in tools and let your local model actually do things - Auto extends context length when you're about to run out. no more silent truncation - Hardware aware model recommendations, so it tells YOU which model your machine can actually run instead of you guessing and crashing your RAM - OpenAI compatible local API server if you want to wire it into your own scripts later This is the local LLM setup you need when you want started ASAP with a great UI: HuggingFace + llama.cpp power, GUI simplicity, and agent ready out of the box. Download from the link in the comments and let me know if you need any help. What's stopping you from running AI locally right now RAM, know how, or just never knew this existed?
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