Local-First AI Agent. Team: @skinbagwbones @sosidudku @schwarzerrttr
California, USA
Joined July 2026
- Tweets648
- Following6
- Followers3.1K
- Likes1.9K
Pinned Tweet
MiniMax M3.1 Flash got 1.5x faster in Atomic Agent!
We ran a Halloween prompt that builds one continuous 3D scene. In Atomic Fusion a local Qwen 3.8 27B wrote the plan, MiniMax did the coding and it was done in 8 minutes!
Try Atomic Agent for FREE -> atomicagent.io
MiniMax M3.1 Flash: Space Performance + Bunny Speed
🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash.
💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed.
API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens.
Model weights will arrive on Oct 23.
API: platform.minimax.io
M Plan: minimax.io/m-plan
MiniMax Code for desktop: agent.minimax.io/download
Atomic Agent retweeted
Meet Atomic VPN for $2/month
– 6,500+ servers in 88+ countries
– zero logs
– 5 devices on one account
Download Atomic VPN - link in comments
Meet Atomic Agent Desktop!
✦ Local AI-first agent for Mac, Windows & Linux
✦ 4x bigger context window on local models with TurboQuant
✦ Atomic Fusion orchestrates cloud and local models to reduce costs
Download Free & Private Agent → atomicagent.io
Atomic Fusion cut GPT-6.1 Sol’s API bill by 77% 💥
We tested cloud-only, local-only and orchestrated builds. Cloud GPT-6.1 made a plan and orchestrated local Qwen 3.8 27B, making it cheaper with minimal quality loss
Try Atomic Fusion -> atomicagent.io
Sonnet 5.5 orchestrated a local Qwen 3.8 27B cutting the bill 2.7x!
Same build on Sonnet 5.5 cost $1.83, while local Qwen was $0 and got one scene right. With Atomic Fusion, Sonnet planned and Qwen coded, the bill fell to $0.67.
Atomic Fusion -> atomicagent.io
Try it yourself: /runmode fusion in the terminal
github.com/AtomicBot-ai/atom…
Atomic Agent v0.6.5 is out!
✦ Multi-agent orchestration with Fusion
✦ Import from Claude Code, Codex and more
✦ Auto-switch to local models if cloud fails
✦ Approve commands from Discord
Available on Mac, Windows and Linux: github.com/AtomicBot-ai/atom…
Atomic Agent connects 1500+ apps powered by @composio
✦ Set up with one API key
✦ Connect Gmail, Slack, Notion, GitHub and more
✦ Send emails, create tickets and manage files
Available on Mac, Windows and Linux: github.com/AtomicBot-ai/atom…
Atomic Agent v0.5.4 is out!
✦ Hugging Face and Ollama models
✦ Coding mode: plan, auto, or ask-first
✦ Context window: set the size of memory
✦ Fully redesigned TUI: scroll, click, select, copy
✦ Import data from Hermes, OpenClaw or ClaudeCode
Built for local models: github.com/AtomicBot-ai/atom…
The new Fable 5.1 built three games and did it 7% cheaper!
We gave the same prompts through Atomic Agent:
✦ Sticky ball that rolls up everything in its path
✦ Capybara surfing down a river, Subway Surfers style
✦ Dumpling running an endless conveyor dodge
Outputs:
✦ Claude Fable 5, via Atomic Agent: $7.65
✦ Claude Fable 5.1, via Atomic Agent: $7.08
Run it through an agent and that cache discount is where the money hides. The loop reads the file, watches it break, and pulls the whole context back in to fix it, over and over. Every one of those re-reads costs a quarter on 5.1 of what it cost on 5. Same games on screen, and the newer version just spends less to get there. Across three builds the loop paid for the upgrade on its own.
Atomic Agent made GLM 5.3 better and it costs only 77 cents more!
We ran three prompts:
✦ Teddy bear falling apart mid-run
✦ Frog popping
✦ Snowman melting in a sauna
Outputs:
✦ GLM 5.3, API only: 543,290 tokens, $2.39
✦ GLM 5.3 + Atomic Agent: 1,091,380 tokens, $3.16
An API call ends the moment the model stops typing. Atomic Agent keeps going: it runs the output, reads what came back, and hands the model its own failure to fix. The model picks the actions, the runtime owns the loop, the state and the stop conditions.
The parts that matter here:
✦ one JSON array of tool calls per step, up to 15, reads running in parallel instead of one round trip per action
✦ results compressed before they go back into context, so the window holds the work instead of the transcript
✦ repeated actions get a warning at 3 and a veto at 5, so a stuck model stops instead of burning your budget in a circle
GLM-5.3 is now open-weight.
Our most capable model for agentic coding and cyber defense is now available to download, run, and customize.
Weights: huggingface.co/zai-org/GLM-5…
Tech blog: z.ai/blog/glm-5.3
Qwen3.8-Flash-Next from @Alibaba_Qwen has day-0 support in Atomic Agent!
125B main model, 51B N-gram embeddings, 6B activated per token and 62.5 on SWE-bench Pro.
We gave it a folder and it checked every file to sort them by content. Total: 25 steps + 230K tokens ≈ $0.07
⚡Meet Qwen3.8-Flash, a multimodal MoE and an early preview of the Qwen4 architecture, now open-weight!
The production version Qwen3.8-Flash will be available soon via QwenCloud API at just $ 0.16/1M input tokens and $ 0.47/1M output tokens.
125B parameters + 51B N-gram embeddings, with just 6B activated per token. Unmatched cost-efficiency.
What's new: 🥳
- Next architecture: GDN + QSA hybrid attention, Gated Residual, N-gram Embedding & Muon optimizer, serving as a precursor to the architecture used in Qwen4.
- Dramatically lower training and inference costs: trained at just 1/9 the cost of Qwen3.7-Plus, while outperforming it across the board with especially strong gains in coding and office tasks.
- Strong performance: scoring 58.7 on DeepSWE 1.1, 62.5 on SWE-bench Pro, 73.9 on CoWorkBench, 84.5 on AndroidWorld, and 95.7 on MathVision (with CI).
- 262K native context, extensible to 1M with YaRN.
We’re also releasing the weights for Qwen3.8-Flash-Next, giving the community an early look at the new architecture we’re exploring for Qwen4.🚀
We can't wait to see what you build with Qwen3.8-Flash!👀👇
- Blog: qwen.ai/blog?id=qwen3.8-flas…
- Technical Report: github.com/QwenLM/Qwen3.8-Fl…
- Hugging Face: huggingface.co/Qwen/Qwen3.8-…
- ModelScope: modelscope.cn/models/Qwen/Qw…
