Tari Ibaba

Wow so we just discovered who was behind the incredible Ox Alpha stealth model all along!

It turned out to be none other than GLM-5.3 Flash from Z.ai in the end — so many Google fanboys were really disappointed from it not being Gemini.

It’s an absolutely incredible model that completely destroys its GLM-5.2 predecessor and other top-tier models — while being several times faster.

GLM-5.3 Flash has one of the lowest hallucination rates of all the models — and it’s more powerful than all the models in this list with lower hallucination rates:

GLM-5.3 Flash ranked an incredible 4th place on the AI Arena web dev leaderboard — despite several times cheaper than all the models above it:

“GLM-5.3-Flash delivered a major leap on DeepSWE v1.1, scoring 63.4 versus GLM-5.2’s 46.2 and outperforming Claude Opus 4.8 (58.0) and DeepSeek-V4-Vision-Exp (59.3), while coming within reach of Gemini 3.7 Flash (65.3) and GPT-5.6 Terra (69.6) — a remarkable result for an open-weight model built around efficient inference.”

And the shocking thing is that it does all this while being more than 900% more efficient than GLM-5.2 in operating costs.

GLM-5.3-Flash delivers a remarkable leap in cost efficiency, operating at roughly one-tenth the cost of GLM-5.2 — an improvement of more than 900% in operating-cost efficiency

And the secret behind this massive improvement lies in its state-of-the-art architecture.

GLM-5.3 Flash uses a new sparse attention technique to dramatically improve how the underlying transformer model processes text.

By combining linear attention for efficient local processing with sparse attention for selective long-range retrieval, GLM-5.3-Flash maintains strong long-context performance while significantly reducing the computational cost of traditional attention

Z.ai also built something called IndexPool to drastically reduce the amount of memory it uses — making it more than 4 times more efficient than even the standard GLM-5.3.

Through IndexPool, GLM-5.3-Flash compresses key representations across long contexts, cutting attention computation by roughly threefold and shrinking the KV cache by 4.4× compared with GLM-5.3 — significantly reducing the computational and memory costs of long-context inference.

Then there’s the Mixture-of-Experts design which lets it only activate the actual parameters it needs for every task.

Through its Mixture-of-Experts architecture, GLM-5.3-Flash contains 320 billion parameters while activating only 18 billion for each token — delivering the capabilities of a massive model without requiring its entire parameter set for every computation

So with all this you can definitely see why its so much more efficient than so many other models.

And with all this innovation and intelligent, it still comes with completely open weights — with the highly permissive MIT license.

Released as fully open weights under the permissive MIT license, GLM-5.3-Flash gives developers broad freedom to deploy, modify, and commercialize the model, with native integration across leading inference frameworks including SGLang and vLLM

So you can download and run it locally — though this one is definitely way too massive for an everyday PC.

And there’s something really fascinating that happened.

Before the official launch Z.ai secretly released GLM-5.3-Flash as Ox Alpha through OpenCode and OpenRouter. Millions of developers tested the model without knowing who was behind it.

And now Z.ai is saying that they processed all that traffic on state-of-the-art Chinese-made AI chips.

And it’s not just about the chips themselves — but how they made the made the model run on the chips.

They used sophisticated techniques like quantization and parallelism — to reduce memory usage and elevate performance.

By combining W8A8 quantization, tensor parallelism and specialized worker pools for different stages of inference, Z.ai built a highly optimized serving system that improved end-to-end performance by 3× while making large-scale deployment significantly more efficient

Z.ai says GLM-5.3 helped its engineers improve a lot of code and infrastructure they used to run GLM-5.3-Flash.

That means we are also getting closer and closer to the possibly ominous singularity stage — where AI starts becoming intelligent enough to improve itself — which leads to an more intelligent AI that can improve itself even better and so on.

All in all this is going to be such a game changing model in the AI ecosystem.

This is incredible.

Anthropic just made an insane change to Claude Code that will massively improve your productivity as a developer — although it might seem scary at first.

Claude Code now automatically executes commands and make tools calls by default when coding — no more stopping to ask for permission.

It won’t do this anymore:

And I know at first glance some developers will be seriously worried about this — won’t this potentially lead to disaster like file/database wipeouts or corruption from unsafe tool calls?

But actually this line of thinking is completely wrong — especially from what Anthropic discovered from their internal research.

In internal testing conducted by Anthropic across 1,053 paid participants, humans caught disguised dangerous commands only 13.6% of the time. After 50 consecutive permission prompts, detection dropped to roughly 5% as approval fatigue kicked in.

So it looks like it’s actually we humans we need to be concerned about when it comes to verifying commands.

So Claude Code now uses a background classifier to automatically know which commands are safe and which aren’t.

And the difference was absolutely insane — the automatic system was almost 7 times better at catching dangerous commands

Claude Code’s background classifier demonstrated a substantial security advantage, identifying and blocking 89% of destructive commands that might otherwise have been executed.

So of course once they saw the massive improvement it only made sense to make it the default in versions of Claude Code — Pro, Max, and Team.

And this change gives us so many benefits beyond just security.

Claude Code is no longer going to stall in its execution just to ask you to approve a command — you can finally experience the full value of long-horizon autonomous coding.

Anthropic reports that teams using it ship roughly 25% more pull requests because long-running agents can continue working without repeatedly waiting for human approval.

I found it pretty annoying at times — I would give Claude a prompt and expect it to be done when I come back — only to find out that it has barely even done anything — all because it’s waiting for me to approve a command.

I’m not the only one too:

Now I will have complete certainty that when I come back I will see results.

And also the background classifier is completely free — zero cost on all the complex logic or token usage of checking if a command is safe or not.

And free for all plans too.

Anthropic fully absorbs the computational cost of Claude Code’s background classifier, allowing Pro, Max, and Team users to benefit from continuous automated security checks without consuming any additional usage allocation.

So you don’t need to spend more just to be able to code with peace of mind.

And Auto Mode isn’t only looking for obviously dangerous shell commands — also helps defend Claude against indirect prompt injection.

So like if Claude reads a third-party package, webpage, or scraped file that has hidden instructions telling it to ignore the user and steal credentials.

Claude could potentially interpret those instructions as legitimate.

So this new Auto Mode adds another security boundary — the classifier always makes sure to compare Claude’s proposed action with the user’s original intent.

And this is especially important for autonomous agents because the more information they take in from the outside world, the larger their prompt-injection attack surface becomes.

Of course an AI supervising another AI creates its own problem — what happens when the security classifier repeatedly blocks Claude due to false positives?

Anthropic took this into consideration and built in an escalation mechanism.

If the classifier blocks three consecutive commands or reaches 20 blocked actions during one session, Claude Code automatically exits autonomous execution and returns control to the human.

One rejection might simply mean Claude chose a risky approach.

Twenty means something probably deserves human attention.

Auto Mode isn’t perfect tho — Anthropic still recommends human oversight for high-stakes production operations.

But the direction is really fascinating.

We originally put humans in the loop because we didn’t trust AI — then humans started clicking “Approve” without reading.

So now Anthropic has decided to put another AI in the loop to supervise the first one.

The new Grok 4.6 model is amazingly good at coding.

Elon Musk’s xAI trained it on billions of real developer interactions and and code editing patterns — thanks to their recent acquisition of the Cursor IDE.

It now performs as good as top-tier models like GPT-5.6 and Opus 5 the vast majority of the time — yet it’s multiple times cheaper.

And Grok 4.7 is already coming very soon too — with big promises of it “exceeding all current models” from Musk.

Grok 4.6 scores an incredible 5th place on the AI Arena Leaderboard — despite being much cheaper than most of the model above it — only Qwen 3.8 Max is as cheap as it (same price).

On CursorBench v3.2, Grok 4.6 Extra High took the top spot with 70.8%, edging out Fable 5 Max at 70.5% and Opus 5 Max at 70.0%, while comfortably beating GPT-5.6 Sol Max at 67.2%

It comes with major upgrades in software engineering and long-horizon agentic coding.

Grok 4.6 combines frontier-level benchmark performance with aggressive pricing, long-running agent capabilities, deep Cursor integration, and stronger visual application development.

It can carry out the most complex tasks in the most complicated codebases imaginable.

Grok 4.6 is designed to execute extended, multi-step workflows. It can use tools, navigate large codebases, research unfamiliar topics and maintain context across long sequences of actions.

It also has much stronger self-verification.

Grok 4.6 is much better at checking its own homework: xAI reports more self-testing and verification on long-running tasks, alongside a jump from 54% to 65.9% on DeepSWE v1.1 and 47.1% to 57.5% on APEX-Agents versus Grok 4.5.

And this fixes one of the biggest weaknesses of autonomous AI agents: small mistakes accumulating across long workflows.

It also has a 500K context window — something I was really surprised about — cause these days 1 million is like the standard. But it’s still quite a lot.

With a 500,000-token context window, Grok 4.6 can keep roughly 375,000 words of text in context at once—equivalent to about five full-length novels—giving long-running agents far more room to work without losing the plot

It’s a lot more intelligent than it’s predecessor — even though it costs exactly the same — giving much more intelligent per unit cost.

It scores 61 on the Artificial Analysis Intelligence Index, up from Grok 4.5’s 56 and placing it among the highest-performing models in SpaceXAI’s published comparison.

Grok 4.6 was trained from the ground up on the actual real-world code and developer patterns from Cursor — so it could achieve the best results possible as a coding agent.

Its training included high-quality engineering data and supervised fine-tuning trajectories covering software engineering, reasoning, STEM and agent workflows.

It used sophisticated techniques like reinforcement learning to deeply understand how we actually code and work as developers.

SpaceXAI then applied agentic reinforcement learning across environments including general coding, kernel optimization, web development and computer-aided design.

It also amazing at coding with visual input data — which makes it especially powerful for web design and frontend development.

Give it a high-level product idea, and the model can establish an application’s structure and visual language, implement its core interactions and then refine the result through subsequent iterations.

You can build unbelievably sophisticated games and 3d web experiences with very little prompting.

You can just describe what you want to build and let the Grok-4.6-powered agent work out all the architecture, interface and implementation.

xAI is still a serious contender in the AI race — and Grok 4.6 is definitely worth giving a try, for coding and many other use cases.

This is incredible.

Claude Code just got a new command that makes it so much easier to build beautiful and breathtaking apps.

The new /design command brings all the most powerful features of Claude Design directly into Claude Code –to make designing an app as effortless as possible.

It’s perfect for whenever you want to add a new visualizable feature like a new screen.

The /design command in action in Claude Desktop — works in Claude Code too:

Before you’d just ask Claude to build the feature and then it gives the result — which you may or may not be satisfied with.

But now things have gotten a lot more sophisticated.

Now with the /design command Claude can generate and show you several visual directions in which it can take to implement what you’re asking:

The /design command makes multiple design suggestions:

You can inspect and refine them — then turn the direction you choose into frontend code.

The most interesting part of /design is that it doesn’t have to commit to a single interpretation of your prompt.

Claude can generate several distinct visual directions on one canvas. One dashboard might be dark, dense, and technical. Another might be minimalist and editorial. A third one might emphasize large charts and simplified navigation.

The artboards can also be interactive rather than static mockups.

Forms, calculators, charts, and other UI elements can demonstrate the behavior for you — giving you a chance to test the micro-interactions before you commit to an implementation.

You can also make lightweight edits directly on the canvas like adjusting text, spacing, layout, or individual elements. That makes the design itself part of the conversation with Claude.

Adjusting the generating design with auto-generated tweaks:

Another amazing ability it has is how it resolves all the unclear requirements before it starts generating — it doesn’t just make irritating assumptions.

A vague request like “make me a modern SaaS dashboard” leaves dozens of decisions unspecified: typography, density, brand feel, navigation style, and overall visual direction.

Instead of silently falling back to familiar AI design clichés the design workflow can ask targeted questions about themes, layout preferences, branding, and constraints first.

And the best part is how it can you use your existing codebase and all the visual components in as context.

Claude can inspect your project and figure out all the styling conventions, CSS variables, Tailwind configuration, design tokens, typography, and reusable UI components.

So instead of generating a mockup that looks completely detached from the current application, /design can now work directly with your existing design language.

There’s no more need to export a screenshot, manually rewrite a design specification, or describe the chosen layout all over again.

That means the all type scale, spacing, component choices, and styling you select can flow directly into your frontend code.

It makes experimentation so much easier — and drastically expands the limits of your creativity.

Making it incredibly useful as a solo developer where you have to be a product manager, designer, and engineer at the same time.

The crazy thing is that nobody knows exactly who is behind it — just a cryptic name of Ox Alpha.

But it’s already scoring amazingly in several benchmarks — even beating Claude Fable and GPT-5.6 Sol in critical areas.

An initial evaluation using 10 DeepSWE coding tasks found that Ox Alpha completed eight, producing an 80% result on that subset. In the same comparison, Claude Fable 5 scored 65%, GLM-5.3 62%, Grok 4.6 62%, and GPT-5.6 Sol 52%.

Some people think it’s Gemini 3.5 Pro or 4, others are saying it has to be GLM-6.

But they’re literally offering the first 100 trillion tokens of usage for free — so clearly they have to be a huge company with deep pockets.

Backed by a provider claiming an unprecedented serving capacity of 100 trillion tokens per day, Ox Alpha offers near-unlimited free inference at an industrial scale that allows developers to run massive agentic workflows and long-horizon tasks without compute bottlenecks

And of course it has a 1 million token context window — something that’s quickly becoming standard across models.

Maybe they temporarily removed the “safeguards” to avoid being detected like this? 😅

One of Ox Alpha’s biggest features is its 1,048,576-token context window, alongside an output limit of approximately 131,072 tokens.

So it will have more than enough room for all your massive codebases.

That makes it particularly interesting for AI agents. Developers can feed the model substantial software repositories, documentation, issue histories, and specifications in a single context.

It also has full multimodal reasoning.

Ox Alpha isn’t limited to text. It natively supports text, images, and video, alongside tool calling and structured outputs.

So for example you can provide a UI wireframe and ask the model to implement it, or feed it a video recording of a software bug instead of explaining the problem manually.

It also uses the sophisticated Mixture-of-Experts (MoE) architecture — which let’s it achieve the intelligence of a massive model at a fraction of the computational cost and latency.

By deploying a Mixture-of-Experts architecture that routes tokens through approximately 40 billion active parameters out of a massive 744 billion total parameter pool, Ox Alpha delivers the world-knowledge capacity of a ultra-large model with the high inference speed needed to process up to 100 trillion tokens daily

I’ve really been noticing this growing trend of releasing stealth AI models — and I can definitely see why they do this.

When you don’t know who exactly is behind the model, you’re less likely to suffer from a preconceived bias of models from a certain company being better or worse.

Releasing a model without revealing its creator gives AI companies an enormous blind test. Developers benchmark it, discover weaknesses, stress-test its infrastructure, and compare it with competitors without being influenced by the company behind it.

And of course it’s also great for marketing — especially in a case like this that the model is doing particularly well — like a mysterious genius that every is going crazy about.

It also creates powerful marketing. Instead of announcing Ox Alpha with a conventional launch event, its creators have given the internet a mystery to solve.

It’ll be really interesting to see who the creators end up being and what more they have in store for us and the AI race.

It was only a matter of time.

This new local model is sending shockwaves across the entire AI ecosystem.

The new Qwen 3.8 27b model just shattered the boundaries of everything we thought was possible with local AI models.

A model that’s small enough to run on a local Macbook for FREE — yet matching up amazingly with supercomputer-powered models like Claude Opus 4.6 Max.

You can power Claude Code and all your agentic tools at zero cost.

A local model is now beating all these recently released top-tier models that run in the most powerful data centers in the world:

Qwen3.8-27B delivers frontier-level agent performance at a fraction of the size, scoring 61.7% on SWE-bench Pro and 84.3% on OSWorld-Verified versus Claude Opus 4.6 Max’s reported 53.4% and 72.7%, while also surpassing Qwen3.7-Plus (57.6% and 73.3%) on key agentic benchmarks

It’s really exciting to see where the AI landscape is heading — we’ve seen open-source models catch up with closed-source models — now we are starting to see local models start to give major competition to cloud-hosted models.

This is by far the most efficient AI model ever created — we’ve never seen this level of intelligence from a model this tiny.

At 27 billion parameters, Qwen3.8-27B can be compressed to roughly 16–19GB with GGUF quantization — putting it within reach of high-end consumer GPUs like the NVIDIA RTX 4090 and 5090, as well as Apple Silicon Macs with enough unified memory.

The improvements are unbelievable in coding and autonomous agent tasks.

Qwen3.8-27B scores 61.7% on SWE-bench Pro, up from 53.5% for Qwen3.6-27B and ahead of Qwen3.7-Plus at 57.6%. On DeepSWE 1.1, the jump is even larger: 42.2 versus 13.3 for its predecessor.

Computer use is equally impressive.

Qwen reports 84.3% on OSWorld-Verified and 81.9% on AndroidWorld, benchmarks that test whether AI agents can visually understand and operate desktop and mobile interfaces.

Qwen3.8-27B is built from the ground up to see an interface, reason about what to do, use tools, write software and execute long sequences of actions.

And does all of this with breathtaking speed — thanks to its unusual hybrid architecture, which combines something called Gated DeltaNet with conventional Gated Attention.

Normally, AI models get super slow and laggy when you feed them long documents or big files — because the their transformer algorithm usually forces them to re-read and compare every single word to every other word.

Qwen 3.8 fixes this slowdown by using a smart 3-to-1 tag-team system across its 64 layers:

• 3 Fast-Skimmer Layers (DeltaNet): These three layers zip straight through long lines of text at high speed, saving a ton of computer memory.
• 1 Deep-Thinking Layer (Standard Attention): The fourth layer steps in to double-check the details, making sure the AI still understands exact meanings and relationships between words.

This makes it way more efficient at processing massive amounts of text — which invaluable for the 1 million token context window support that it has.

It also optimizes its speed with Multi-Token Prediction (MTP) — which lets it predict and verify multiple tokens at once rather than generating everything strictly one token at a time.

Qwen 3.8 also gives you granular control over reasoning.

You can adjust reasoning_effort between xhigh, medium and low — or disable extended thinking entirely using enable_thinking.

So you can low reasoning for simple operations, then switch to xhigh when debugging something difficult — or disable thinking altogether when maximum speed matters.

Qwen3.8 also supports preserve_thinking — which lets its reasoning state persist across multi-turn conversations.

For long-running agents, this massively reduces redundant reasoning and improves KV-cache utilization — instead of repeatedly reconsidering the same information.

That level of sophistication in runtime reasoning control is highly unusual among open models — especially for its incredibly lean size.

Qwen3.8-27B is also a native vision-language model — capable of understanding text, images and video.

It scores 91.1% on OmniDocBench 1.5, demonstrating strong performance on complex documents, while its vision system can handle charts, STEM diagrams, screenshots and other visual information.

Qwen also supports hour-scale video understanding, with configurable frame sampling allowing developers to balance detail against computational requirements.

Put everything together and the significance becomes clearer.

A 27 billion parameter model can now rapidly write and debug software, operate computer interfaces, analyze documents, understand images and video, reason autonomously, maintain thinking state and process enormous contexts—all on hardware you can own.

We can clearly see where all this is heading — frontier-class capabilities are beginning to escape the datacenter.

Wow this is huge.

Elon Musk’s xAI just released a brilliant new coding agent with unbelievable new features — and the developer community has been going absolutely wild.

Grok Build is here to unleashed the full power of the most advanced Grok models in every software development task imaginable.

With a new goal-centric plan mode and headless automation mode

And these are just a few of everything this new Grok Build brings to the table.

Sub-agents working in parallel in Grok Build:

Let’s take a look at 5 of these features and the massive benefits they provide in our development workflows.

1. Industry-leading 2 million token context window

Context is still biggest limitations of current coding agents.

Large projects often contain huge codebases, extensive documentation, configuration files, and dependency trees that exceed what most AI models can effectively keep in memory.

Grok Build is powered by top-tier Grok models like Grok 4.20, which features a 2 million token context window, let’s us give it:

• Entire codebases
• Large documentation sets
• Technical specifications
• Dependency chains
• Historical implementation notes

all within a single session.

Instead of constantly re-explaining project structure, the model can maintain a much deeper understanding of the system it is working on.

2. Headless automation mode

Headless automation will completely transform how you approach development.

Instead of requiring an interactive terminal session, Grok Build can run inside:

• CI/CD pipelines
• Cron jobs
• Automation scripts
• Build systems

This allows engineering teams to automate routine development work.

For example, Grok Build could:

• Review pull requests overnight
• Refactor legacy code
• Update documentation
• Identify technical debt
• Perform maintenance tasks

without requiring a developer to actively supervise every step.

3. Plan mode lets you see everything

One of the biggest frustrations with autonomous coding agents is that they can spend twenty minutes modifying files before you realize they misunderstood the task.

Grok Build introduces a structured Plan Mode to solve this problem.

Before editing a single file, the agent generates a detailed execution plan showing:

• Which files will be modified
• The implementation steps
• Dependencies between tasks
• Areas of potential risk

Developers can review the plan, rewrite steps, leave comments, or approve it entirely.

Only after approval does Grok Build begin making changes, which are then presented as clean Git diffs.

So we end up with workflow that keeps humans in control while still benefiting from automation.

4. Parallel subagents and Git worktrees

Complex features often require changes across multiple parts of a project.

Rather than handling everything sequentially, Grok Build can split a large task into smaller pieces and launch up to eight specialized subagents to work simultaneously.

To avoid conflicts, each subagent operates inside its own Git worktree and branch.

This provides several advantages:

• Faster execution through parallel work
• Isolation between agent tasks
• Fewer merge conflicts
• Easier review and validation

Instead of multiple agents fighting over the same files, each works independently before the final results are merged together.

5. Works with all the tools you already use

Your skills, your hooks, your AGENTS.md, your plugins, everything.

MCP

Grok Build supports Anthropic’s Model Context Protocol out of the box.

This allows teams to connect the agent directly to:

• Internal APIs
• Proprietary databases
• Documentation systems
• Custom tools and workflows

Rather than forcing organizations to change their infrastructure, Grok Build can plug into what already exists.

ACP (Agent Communication Protocol)

It also supports Agent Communication Protocol (ACP).

ACP enables external tools and IDEs to communicate directly with the agent environment, making integrations with platforms like VS Code, Cursor, JetBrains, and custom developer tools significantly easier.

What makes Grok Build interesting isn’t any single feature — it’s how these features work together.

The massive context window helps the agent understand entire systems. Plan Mode adds transparency and control. Parallel subagents accelerate execution. MCP and ACP provide extensibility. Headless mode enables automation beyond the desktop.

Together, they push AI beyond simple code generation and toward something much closer to a true software engineering agent.

DeepSeek just released a revolutionary new open-source Claude Code competitor — but it’s so much more than that.

The new DeepSeek Harness is not just trying to be a coding agent.

It’s not even trying to compete in models or features or settings or commands.

It’s trying to compete at the AI operating system layer — the harness layer.

It’s the harness layer that really makes AI coding possible — the layer that transforms any model into an autonomous agent.

AI models on their own are very intelligent but can only chat — they need a high-quality harness to let them read files and make network requests and extend themselves with plugins and skills — like what an Operating System does for a very powerful processor.

So DeepSeek Harness is trying to be the most powerful operating system ever made — that maximizes any model to its full intelligence potential to give you the best and fastest results possible.

It’s unbelievably flexible — you can basically replace or customize every single thing.

Tools like Claude Code and Cursor let you swap models and add new extensions.

But DeepSeek Harness goes much much further — everything is a plugin — everything:

• Model adapters
• Permission systems
• Session storage
• File-system access
• Tool definitions
• Agent orchestration logic
• Even the agent execution loop itself

List of connected plugins in a DeepSeek Harness:

You are not locked into a predefined way of building agents — you can even connect it with other harnesses like Claude Code.

You can assemble your own agent stack from interchangeable components — we’ve had tech stacks, now we have agent stacks in 2026.

You can even embed an entire agent system inside a larger workflow — Agent-in-Agent Orchestration.

For example a DeepSeek Harness workflow could:

• Use Claude Code for software development
• Call OpenAI Codex for specific coding tasks
• Route research to another agent
• Coordinate everything through a parent orchestrator

And the harness itself is not limited to DeepSeek models.

It supports OpenAI-compatible API endpoints — which allows you to mix and match models across providers.

So like for example:

• DeepSeek for reasoning
• A vision model for image analysis
• Claude for writing
• Local open-source models for privacy-sensitive tasks

The framework focuses on orchestration rather than locking users into a single model ecosystem.

Four purpose-built runtime modes

Instead of changing model intelligence, DeepSeek Harness changes the environment around the model.

Standard mode

General-purpose execution for everyday workflows.

Code mode

Optimized for long-running software engineering tasks, including:

• Planning
• Coding
• Debugging
• Testing
• Multi-step implementation

Minimal mode

A lightweight, sandboxed environment designed for benchmarking and evaluations such as:

• SWE-bench
• TerminalBench
• Research experiments

Creator mode

Perhaps the most experimental runtime.

Creator Mode allows agents to:

• Inspect plugins
• Modify plugins
• Compose new plugins
• Reconfigure parts of their own environment

Rather than simply using tools, agents can actively reshape the harness itself.

State-of-the-art generative UI

DeepSeek Harness also includes dsh-genui, a system for generating interactive user interfaces.

Instead of responding only with text or Markdown, agents can render:

• Graphs
• Dashboards
• Visualizations
• Interactive widgets
• Sandboxed mini-applications

This allows agents to present information through purpose-built interfaces rather than lengthy text outputs.

Unprecedented transparency

A major focus of DeepSeek Harness is visibility.

The framework ships with a local web interface that exposes operational details often hidden in other agent systems.

Live operational metrics

Developers can monitor:

• Token usage
• Tokens per second
• Tool calls
• Step counts
• Total turns
• Prompt cache hit rates

All metrics are available in real time.

Interactive trajectory timelines

The UI also provides a detailed audit trail showing:

• Every reasoning step
• Tool inputs and outputs
• Sub-agent activity
• Execution latency
• Context assembly details

This makes debugging, optimization, and governance significantly easier.

Built for long-running, low-cost agents

Long-running agent workflows are typically expensive because context grows over time.

DeepSeek Harness tackles this through:

• Optimization around DeepSeek V4-Flash
• Aggressive prompt caching

In extended sessions, cache hit rates reportedly reach as high as 95–100%. As a result, large portions of prompts and conversation history can be reused instead of being repeatedly processed.

What this means is huge as a developer:

• Multi-hour coding agents become affordable
• Multi-agent systems can run continuously
• Complex workflows incur far lower costs
• Long context histories become less expensive to maintain

DeepSeek Harness is not trying to be just another AI agent framework.

It is attempting to become a flexible orchestration layer for the entire agent ecosystem, with key innovations like:

• A true “everything is a plugin” architecture
• Agent-in-agent orchestration
• Model-agnostic composability
• Four specialized runtime modes
• Native generative UI support
• Deep operational transparency
• Extremely cost-efficient long-running execution

As AI systems become more complex and multi-agent workflows become more common, orchestration is starting to matter as much as model intelligence itself.

DeepSeek Harness is one of the strongest signals so far that the future of AI infrastructure will be built around composable systems, not just increasingly powerful models.

GLM-5.3 is a very unconventional upgrade — no one should ignore this and what it means.

Z.ai (the makers) refused to follow the same old formula of training a bigger model and spending more and more on compute.

Like as far as weights and parameters go, this is the exact same model as GLM-5.2 (and still open-source btw).

But somehow it’s just better in every single way — with an unbelievable 50% performance leap and scary coding and hacking ability that now surpasses even Claude Mythos 5.

GLM-5.3 beats every single model in the official Terminal-Bench 2.1 benchmark — include Fable 5 — despite being several times cheaper:

On DeepSWE, GLM-5.3 scored 66.9, nearly matching Kimi K3 (67.5) and outperforming GLM-5.2 (46.2), while remaining competitive with Fable 5 (69.7) and GPT-5.6 Sol (72.7).

They didn’t build a new model from scratch — they focused entirely on how to make the model learn better from existing data — using sophisticated techniques like reinforcement learning and environment scaling.

A novel approach that has now paid off massively.

Open-source GLM-5.3 goes toe-to-toe with Fable 5 in coding and every other area — despite being several times cheaper:

On Agents’ Last Exam (CLI), GLM-5.3 posted the highest score at 28.5, narrowly surpassing GPT-5.6 Sol (28.6) and Kimi K3 (27.6) to lead the benchmark.

It’s challenging the long-standing believe that better AI performance needs larger base models.

From GLM-5.3 we are seeing that we can get serious results from simply coming with new ways to improve how models learn after pre-training.

We could see major gains across board as more and more AI companies consider this different approach.

The cybersecurity performance is incredible.

GLM-5.3 achieved 84.5% on the CyberGym benchmark, a test designed to evaluate vulnerability discovery, software security analysis, and exploit-chain reasoning, surpassing both Claude Mythos 5 and GPT-5.6 Sol on the benchmark.

It’s very strong at:

• Vulnerability discovery
• Security auditing
• Root-cause analysis
• Multi-step software reasoning

GLM-powered systems identified thousands of real-world software vulnerabilities during internal testing, including critical security flaws in widely used developer tools.

They also designed GLM-5.3 with autonomous agents in mind.

The model supports:

• 1 million token context
• 128,000 token output length

This allows it to work across large codebases, lengthy technical documents, and multi-stage workflows without losing context.

Just as important, it also completes tasks using much fewer output tokens than GLM-5.2 while achieving higher accuracy.

That combination means:

• Lower inference costs
• Faster execution
• Better agent efficiency
• Improved performance on long-running tasks

And as we find ourselves using AI more and more for software engineering, efficiency is becoming just as important as raw intelligence.

GLM-5.3 also changes how reasoning works.

You can turn off reasoning anymore like in some other models — you can only choose between Low, High, and Max.

This reflects a growing industry trend where reasoning is treated as a core capability rather than an optional feature.

The question is no longer whether the model should think, but how much compute should be allocated to solving a problem.

Z.ai also remains committed to releasing model weights publicly.

But because of GLM-5.3’s strong cybersecurity abilities, they’ve had to introduce a new Open Source Shield Initiative — which is meant to:

• Keep defensive security research open
• Support code auditing and vulnerability detection
• Restrict access to high-risk offensive exploitation capabilities

This helps to balance openness with responsible deployment.

GLM-5.3 may ultimately be remembered less for its benchmark scores and more for what it represents.

It shows us that massive gains are still possible without training a new base model.

It sets a new bar for AI-driven cybersecurity research. And it is optimized for the future of long-horizon autonomous agents.

If earlier AI progress was defined by scaling pretraining, GLM-5.3 is showing us that next era could be all about scaling post-training instead.

This Claude Code command is amazing.

It’s somehow like the multi-agent teams feature — but much simpler and more straightforward.

It’s perfect for those large-scale changes that can easily be broken down into smaller tasks — like mass refactoring and codebase migration.

It spins up multiple AI agents to work simultaneously across the codebase — giving you an immense boost in productivity.

Let’s look at the key features that make it so invaluable when working with Claude Code — from autonomous testing and pull requests, to how it combines brilliantly with other Claude Code commands to build a comprehensive engineering workflow.

1. Multi-agent task decomposition

The foundation of /batch is its ability to break large requests into smaller, independent units of work.

When you invoke /batch, Claude Code:

• Analyzes the repository.
• Understands the requested objective.
• Identifies logical boundaries within the codebase.
• Decomposes the work into 5–30 independent work units.

Rather than editing files sequentially, Claude distributes work across multiple agents that can operate in parallel.

For example, a framework migration might be divided into:

• Shared UI components
• Authentication modules
• Routing logic
• API integrations
• Testing infrastructure

By separating work into distinct scopes, /batch can scale far beyond the limitations of a traditional single-agent workflow.

2. The /batch and /simplify power combo

One of the more interesting aspects of /batch is how it complements Claude Code’s /simplify command.

While /simplify focuses on reducing complexity and unnecessary abstractions, /batch can coordinate improvements across an entire codebase.

The combination gives us several advantages:

• Identifies overly complex patterns at scale.
• Reduces unnecessary abstractions and indirection.
• Improves code readability and maintainability.
• Creates greater consistency across modules.
• Eliminates repetitive manual refactoring work.

Instead of simplifying files one by one, you can use /batch to coordinate the effort across dozens of components simultaneously. This makes large-scale codebase cleanup and standardization significantly faster and more manageable.

3. Git worktree isolation

A key feature of /batch is its use of Git worktrees.

Every subagent receives its own isolated worktree instead of sharing a single working directory.

This enables:

• Concurrent development
• Independent validation
• Isolated dependency management
• Reduced merge conflicts

Because each worker operates in its own environment, changes made by one agent do not interfere with another.

A component migration can run in one worktree while another agent updates tests or refactors utilities elsewhere in the repository.

This isolation is what makes large-scale parallel execution practical and reliable.

4. Autonomous testing and pull requests

/batch is more than a code-editing tool.

Each subagent behaves like an autonomous developer responsible for its assigned scope.

Subagents can:

• Implement code changes
• Run test suites
• Detect failures
• Fix localized issues
• Validate completed work
• Create commits or pull requests

Instead of producing one massive repository-wide change, /batch generates smaller, self-contained outputs that are easier to review and merge.

The result is less manual coordination and a cleaner development workflow.

5. Plan-first execution

Before making any changes, /batch generates a structured execution plan.

The plan outlines:

• How the task was divided
• Which files or directories belong to each work unit
• The responsibilities assigned to each subagent
• The proposed transformation for each area of the codebase

Developers review and approve the plan before execution begins.

This approval stage:

• Provides visibility into the process
• Prevents unexpected repository-wide changes
• Keeps humans in control of major modifications

Rather than blindly applying edits, /batch follows a plan-first approach designed for large-scale engineering work.

Final thoughts

Claude Code’s /batch command represents the broader shift single-agent coding assistance to coordinated multi-agent software engineering.

With key features like:

• Intelligent task decomposition
• Parallel AI execution
• Git worktree isolation
• Autonomous testing
• Pull request generation
• Human approval checkpoints

It enables repository-wide transformations at a scale that would traditionally require significant manual effort.

Software is no longer being built by a single AI agent, but now by a coordinated systems of specialized agents working together toward a common objective.