GLM-5.3 Beats Fable 5 for Less Money

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Together Compute published DeepSWE numbers for GLM-5.3 and Fable 5, and the headline is hard to ignore: 87.6% solved for about $16 versus 69.7% for $21.63.

There is one real caveat: the GLM number comes from four attempts, not a single shot. But the timing makes it worth paying attention: the Financial Times reported the same day that Anthropic's most powerful model is losing ground to cheaper alternatives.

The benchmark story and the business story are pointing in the same direction: if you route coding tasks, the default model deserves a retest.

x.com/togethercompute

🚀 Shipped

Empero now offers a free OpenAI-compatible community endpoint for Qwen3.8-27B-FP8. Zero setup to point a client you already wrote at a 27B open model and see how it handles your actual prompts tonight. No uptime, rate limits, or data-handling terms were stated, so this belongs in a test harness for now.
x.com/EmperoAI

Archal launched API sandboxes built for AI agents, with Slack, Linear, Datadog, and 20+ other stateful environments for CI and evals. This is the part most teams fake with mocks, even though realistic third-party state is where agents usually break first.
x.com/AidanTiruvan

terminal-code runs VS Code inside your terminal by combining code-server with terminal-browser. If your agents already live in a shell, this keeps the editor, diffs, SSH sessions, review flow, and settings import in the same pane.
terminal-code.com

Bezalel puts memory, email, texting, payments, a computer, sandboxes, and connectors behind one MCP URL. That lets you test a capable agent in Claude Code, Codex, or Hermes without wiring six integrations yourself. It is still alpha, and the connector claims vary, so treat the stack as promising but unproven. bezalel.sh

SenseNova released U1.5-8B-MoT, an 8B any-to-any model on Hugging Face. That is small enough to test multimodal routing locally instead of renting hardware just to see if it fits your stack. The model card does not include benchmarks, so keep it in the “try it yourself” bucket for now.
huggingface.co/sensenova

Audio8 released TTS-Preview-0.1B, which it calls the world’s smallest zero-shot TTS model. At 0.1B parameters, it is small enough to test local voice synthesis per request instead of sending every character to an API.
huggingface.co/Audio8

🧠 Worth Knowing

Anthropic put Mythos 5, its non-public model tier, behind Claude Security for scanning GitHub repos and proposing patches. Anthropic’s strongest code/security model is not going to chat first. It is going to the place where missed bugs cost money. thenewstack.io

Fabien Sanglard published the agent.md he uses to hold LLM-assisted code to his own quality standard. It is a copyable file from someone with a documented standard behind it, which makes it worth diffing against your own instruction file line by line rather than adopting wholesale.
fabiensanglard.net

The MCP maintainers put progressive tool discovery and a standard tool-result contract on the roadmap. Progressive discovery fixes the “100 tools in context before the user asks anything” problem, while one result contract gives client authors a single shape to build against. This is a prioritization doc, not a shipped spec, so treat it as direction, not final API.
blog.modelcontextprotocol.io

Google Cloud published five patterns for long-horizon agents: stable prefixes, background learning, persistent workspaces, explicit failures, and guard chains. These are the bugs that do not crash; they quietly burn cache, lose memory, wipe tools, or mark timed-out sub-agents as done. Steal the checklist before the framework.
https://x.com/GoogleCloudTech/status/2090248297214525569

Someone fine-tuned Gemma 4 12B for tool calling and reported a 2.7x improvement, picking that model because it fits comfortably in 16GB of VRAM. Tool-calling reliability is the failure most teams paper over with retries and validators, and this says it is trainable on a card you already own.
reddit.com/r/LocalLLaMA

OpenAI’s Codex lead Tibo pointed to two drivers behind rate-limit pain: image-heavy long sessions with repeated compactions, and Computer Use getting expensive at p95+. You cannot fix Codex’s limits, but you can stop burning them on avoidable session shape. Keep images out of runs you expect to compact. x.com/thsottiaux

🔧 Clone and Run

 Clone & Run of the Day
Apache Maka is a local-first agent workspace that logs model messages, tool calls, permissions, and termination events as first-class records. The useful part is logging the two things you usually lose after a bad run: who approved what, and how the agent stopped. Still in Apache incubation, so treat it as early infrastructure. github.com/apache/maka

Paseo is an open-source control surface for Claude Code, Codex, Copilot, and OpenCode across local machines and a VPS. If your agent setup is currently tmux, SSH, and too many panes, this is the cleaner version of the layer you are already rebuilding.
github.com/getpaseo/paseo

Anthropic’s claude-plugins-community is a read-only mirror of the Claude Code and Claude Cowork plugin directory. It shows what is actually in the marketplace, and gives plugin builders the official path to get listed.
github.com/anthropics/claude-plugins-community

OpenHuman is an open-source personal AI with local-first memory and agent-fleet orchestration. Most projects give you one or the other; this tries to put the memory store and the agent runner in the same local system. github.com/tinyhumansai/openhuman

agent-safe-pipeline is a reference architecture where agents can propose actions but cannot approve them. It keeps intent capture immutable and sends the final decision to an independent policy layer, which is where agent permissions belong.
github.com/decionis/agent-safe-pipeline

open-slide turns a prompt into an interactive deck by having coding agents write the React components. Generating the artifact as code instead of as a rendered file means the output stays editable by the same agent that made it.
github.com/1weiho/open-slide

Ruflo is an open-source meta-harness for deploying multi-agent swarms and coordinating autonomous workflows. If you want to see what the coordination layer looks like beyond one agent at a time, this is a large implementation to read.
github.com/ruvnet/ruflo

oh-my-subagents adds persistence and tracking to subagent workflows that are ephemeral by default. The reason you cannot answer what your subagent did last Tuesday is that nothing kept the run, and this is a small patch over exactly that gap.
github.com/ringlochid/oh-my-subagents

Awesome LLM Apps (134k+ 🌟 ) is a curated collection of 100+ AI Agents, Agent skills, and RAG apps. It covers models from OpenAI, Anthropic, Google, and open-source models like GLM, DeepSeek, and Qwen that you can run locally on your computer. (Now accepting GitHub sponsorships)

By the Number

Number of the Day
Together Compute’s routing math puts GLM-5.3 at 17 solves per $100 on DeepSWE, versus 3 solves per $100 for Fable 5. That is the number that matters if your agent can retry: not which model wins once, but which model gives you more passing runs per dollar.
x.com/togethercompute

Researchers ran GLM-5.2 753B at 14.9 tok/s on a 96GB GPU, DeepSeek-V4-Flash 284B at 22 tok/s on 32GB, and Qwen3.6-35B at 39.3 tok/s on an 8GB card. If the setup holds, “too big to serve locally” just moved down a tier. The 8GB Qwen number is the one most readers can actually try.
arxiv.org

a16z reports the fastest-growing Codex adopters since February are outside tech: legal up 108x, sales and recruiting 41x, marketing 26x, healthcare 24x. The first non-engineering power users are not “vibe coding.” They are turning repeatable knowledge work into Codex-shaped tasks.
a16z.news 

That’s all for today. Come back tomorrow for the next batch of AI tools, model drops, agent repos, and weird benchmarks worth your time.

If you found one thing to try, share the issue with someone who ships.

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