Tari Ibaba

This is HUGE.

OpenAI just shocked the entire coding world with the new GPT-5.6.

It actually dominated Claude Fable 5 in some really key areas, wow.

GPT-5.6 outshines both Claude Fable and the legendary Claude Mythos in multiple high-profile benchmarks.

Unbelievable scores in the most challenging benchmarks out there.

GPT-5.6 beats out almost every model convincingly in the AI Code Arena Frontend leaderboard, scoring near Joint 1st, with Claude Fable 5

And it’s not just way more intelligent — it also comes with an entire new model family, and multiple new first-party tools to build incredible things with the power of GPT-5.6.

Including a brilliant new competitor to OpenClaw.

This model is so powerful that OpenAI actually had to spend weeks convincing the US government that it was safe enough to release into the wild.

1. A brand new model family

GPT-5.6 outclasses its predecessor in frontend web design:

They’ve completely abandoned their traditional one-model strategy.

GPT-5.6 now comes in three permanent capability tiers that can evolve independently over time.

Sol is the flagship model for advanced coding, research, cybersecurity, and complex agent workflows.

Terra is the balanced everyday workhorse, combining strong reasoning with lower latency.

Luna is highly optimized for speed and cost — making it ideal for high-volume tasks like customer support, translation, and summarization.

Instead of paying flagship prices for every request, we can now choose the right model for each workload.

2. Record-breaking leaps in intelligence and coding ability

For the first time in several months, an OpenAI model tops the highly granular DesignArena frontend benchmarks, with the new GPT-5.6:

GPT-5.6 Sol is now OpenAI’s most capable model.

It sets new state-of-the-art results across several major benchmarks, including TerminalBench, BrowseComp, and Humanity’s Last Exam.

On TerminalBench 2.1, which evaluates real-world software engineering tasks, Sol scored 88.8%, rising to 91.9% in its new Ultra reasoning mode.

On BrowseComp, it achieved 92.2%, while Humanity’s Last Exam climbed to 52.7%, outperforming GPT-5.5 as well as Claude Fable 5 and Claude Opus 4.8. OpenAI also says Sol reaches these scores while using significantly fewer output tokens than previous models.

Perhaps the most impressive breakthrough comes from ARC-AGI-3, a benchmark designed to measure how well AI adapts to completely unfamiliar problems.

Sol became the first frontier model to solve a public ARC-AGI-3 task.

The interesting part isn’t just that it performs better. It’s how it reasons.

When one approach fails, Sol dynamically forms a new hypothesis instead of repeatedly executing the same flawed plan — a major weakness of previous-generation AI agents.

3. So good at hacking it delayed the launch

GPT-5.6 outclasses top Claude models in the extremely long-horizon Agent’s Last Exam benchmarks, achieving the best results for an extreme fraction of the cost:

GPT-5.6’s cybersecurity capabilities became advanced enough to trigger an additional U.S. government national security review before public deployment.

The model shows major improvements in command-line operations, vulnerability discovery, threat modeling, exploit analysis, code review, and automated patch generation.

Those gains required an equally large investment in safety. According to OpenAI, Sol now blocks roughly 10× more potentially malicious cyber activity than previous generations while remaining below the company’s “Critical” capability threshold.

4. Massive efficiency gains = massive cost savings

GPT-5.6 demonstrates an incredible ability to generate complex, highly sophisticated interactive diagrams and visualizations from scratch:

GPT-5.6 isn’t just smarter — it’s substantially more efficient.

Sol is 54% more token-efficient on AI coding tasks than previous reasoning models while still achieving better results. It delivers top-tier performance using less computation, reducing both latency and API costs.

And the pricing reflects that efficiency too.

Sol costs $5 per million input tokens and $30 per million output tokens, while Terra and Luna give you progressively cheaper alternatives for less demanding workloads.

5. ChatGPT Work and multi-agent orchestration

ChatGPT Work using multiple tools and features to get achieve tasks blazingly fast:

GPT-5.6 also powers ChatGPT Work, OpenAI’s new platform built around delegation and getting things done.

Its headline feature is Ultra mode, which can coordinate up to four AI agents simultaneously across parallel workstreams.

Instead of solving a project sequentially, GPT-5.6 splits it into multiple tasks, lets separate agents tackle them in parallel, and combines the results into a finished deliverable. That pushes TerminalBench performance from 88.8% to 91.9%.

ChatGPT Work also integrates directly with tools like Slack, Google Drive, Microsoft Teams, CRM platforms, and local desktop files.

This lets GPT-5.6 spend hours building web apps, updating spreadsheets, generating reports, or completing other multi-step workflows with minimal supervision.

GPT-5.6 is going to seriously revolutionize the AI coding landscape.

With stronger reasoning, industry-leading coding performance, permanent capability tiers, major efficiency gains, and true multi-agent orchestration, it’s one of OpenAI’s biggest releases in years.

Developers who ignore this are seriously wasting a lot of Claude Code’s potential.

/code-review – an incredibly underrated command.

This gem will totally transform the quality and correctness of your codebases — it will find and fix problems in places you never even imagined.

Long before they even think of showing up in production.

Problems that go way beyond the lines you changed — it goes way beyond just reviewing diffs and tell you what it thinks.

With code-review Claude can coordinate multiple AI reviewers, understand your entire repository, automatically fix issues, publish findings to GitHub, and even dynamically adapt its review style to your team’s standards.

How it works

Claude Code’s /code-review command in full-scale action

/code-review analyzes your current local Git diff (staged or unstaged changes) against your main branch.

It uses multi-agent orchestration behind the scenes, looking at your code through multiple specialized lenses (security, logic, performance, and error handling, etc.)

Let’s look at five of the most exciting features.

1. Extremely flexible review intensity

Claude Code lets you control how deeply it reviews your code by specifying an effort level.

The effort level can be one of low, medium, high, xhigh, max, and ultra

Most of the effort levels lead to a standard review with a single analysis of your changes.

But running /code-review ultra takes things to a whole new level.

Instead of relying on one reasoning process, Claude orchestrates multiple specialized review agents in parallel. One may focus on security, another on edge cases, another on architectural consistency, while others look for regressions or runtime issues.

Claude then combines their independent findings into one report, giving you broader coverage than a single review pass.

2. Understands everything single thing

Traditional review tools are mostly diff-aware — they inspect only the code you’ve changed.

Claude reviews those changes in the context of your entire repository.

For example, if you modify a function signature in one file, Claude can trace where that function is used elsewhere and detect broken call sites, regressions, or hidden dependencies—even if those files weren’t part of the Git diff.

It can also surface pre-existing issues that your new changes expose, helping you catch bugs that might otherwise remain hidden.

3. Fixes everything automatically

code-review goes way beyond just identifying problems.

Adding the –fix flag:

creates an automated review-and-repair loop.

After identifying issues, Claude reopens the affected files and writes fixes directly into your local working tree. Instead of just pointing out bugs, it proposes actual code changes that you can inspect before committing.

A major time-saver for routine fixes and minor optimizations that seriously adds up over time.

4. Publish GitHub review comments from your terminal

Claude Code integrates directly with GitHub, so you don’t have to copy review results into your pull request manually.

With GitHub commenting enabled, Claude can publish its findings as native inline PR comments on the exact lines where issues occur.

That means you can review locally, send feedback to GitHub, and stay in your terminal without constantly switching between your editor and browser.

5. Customize reviews with REVIEW.md

Every team has different review priorities.

Claude lets you encode those priorities in a repository-level REVIEW.md file.

For example, you can require every new API endpoint to include an integration test, treat missing authentication checks as critical issues, or limit the number of style suggestions so reviews stay focused on high-value feedback.

Instead of generic recommendations, Claude reviews your code according to rules that match your team’s engineering standards.

Claude Code’s /code-review is much more than an AI-powered linter.

With multi-agent reviews, automatic fixes, repository-wide analysis, GitHub integration, and customizable review rules, it acts more like an AI review platform than a simple assistant.

As AI-generated code becomes the norm, tools that can review, validate, and even remediate that code will likely become just as essential as the coding assistants that wrote it.

This is huge.

Claude Sonnet 5 is seriously revolutionary.

A Claude model that matches up to the most powerful and intelligent models out there — yet several times lighter, cheaper, and faster.

It literally dominated every single non-Claude model in several reputable benchmarks — and shockingly matched up to Opus 4.8.

Only Fable 5 was significantly better in the Arena AI leaderboard.

The normal, non-mini version GPT-5.5 could only beat out Sonnet 5 at its absolute maximum thinking effort (xhigh) on the Artificial Analysis Leaderboard:

And it’s not just about the massively improved intelligence density or efficiency.

Sonnet 5 also fixes a major issue that many developers have faced when dealing with Claude models.

The self-improving and autonomous ability is on another level.

All this with ultra-competitive pricing that will give us massive savings in token costs.

1. Intelligence autonomous multi-step execution and self-verification

This is one of the biggest upgrades in Sonnet 5.

It’s now so much better at carrying out extended sequences of work without constant supervision.

By leaping more than 13 percentage points to hit 80.4% on Terminal-Bench 2.1, Claude Sonnet 5 represents the largest terminal-based engineering jump in the lineup’s history, completely closing the gap with the flagship Opus 4.8

Previous Claude models were excellent at planning — but they still struggled to reliably follow through on complicated workflows involving debugging, testing, and iterative refinement. Sonnet 5 is build from the ground up to stay on track.

The new and improved self-verification is on another level.

It now verifies its own work from top to bottom before returning it.

Instead of generating a fix and hoping it’s correct, the model can reproduce a bug, implement a solution, test the fix, temporarily remove it to confirm the issue returns, and then restore the working version — all without being explicitly instructed to perform each step.

Proactively fixing problems and edge cases you would never have considered.

2. One million token context — but somehow even better

Sonnet 5 also uses an incredible 1 million token context window — making it practical for us to work across enormous codebases, lengthy documents, and large enterprise knowledge repositories.

But here’s what makes it such a big deal now — Anthropic has completely removed the pricing penalty that previously applied to very large prompts.

Earlier Sonnet models charged premium rates beyond 200,000 tokens — but Sonnet 5 offers standard pricing across the entire one-million-token context window, making large-context applications far more economical.

3. Massive improvements in agentic coding capabilities

Anthropic has also made enormous progress in agentic coding.

Claude Sonnet 5 narrows the gap to flagship-level intelligence by surging to a 63.2% pass rate on SWE-bench Pro—marking a major 5.1 percentage point leap over Sonnet 4.6 and landing within striking distance of Opus 4.8’s field-leading 69.2%.

Compared with both Claude Sonnet 4.6 and even the more expensive Claude Opus 4.8, Sonnet 5 is significantly better at handling extended software engineering workflows with minimal human intervention.

It can maintain context across long coding sessions, make coordinated changes across multiple files, recover from mistakes — all while rapidly progressing toward the larger objective.

A super-powered collaborative engineer capable of managing the most substantial development tasks.

4. Adaptive thinking by default

One of the most interesting changes happens before the model even begins generating a response.

Rather than immediately producing an answer — Sonnet 5 now uses adaptive thinking by default.

It automatically decides how much reasoning a problem requires, spending more time on difficult coding and analytical tasks while responding quickly to simpler requests.

We can also control this behavior, adjusting the reasoning effort from Low to Extra High depending on whether they prioritize speed or accuracy.

At its highest settings, Sonnet 5 reaches reasoning and coding performance comparable to Claude Opus while maintaining the efficiency expected from the Sonnet family.

5. Built-in real-time cybersecurity guardrails

Claude models have been getting scary good at finding vulnerabilities in codebases that we once thought were highly secure.

So Anthropic has taken extra precaution to prevent bad actors from being able to use it to exploit vulnerabilities in the most critical software systems.

Claude Sonnet 5 is the first Sonnet-tier model to include real-time cybersecurity protections by default.

It’s specifically designed to refuse requests related to active exploit development, network compromise, and other offensive cybersecurity activities, bringing its safety profile closer to Anthropic’s highest-tier models.

Claude Sonnet 5 is all about making AI much more useful and practical in our everyday workflow as developers.

Its biggest advances lie in how it works: executing long workflows reliably, verifying its own output, reasoning more deeply when needed, handling vastly larger contexts without extra cost, and operating with stronger built-in safeguards.

Claude Skills have had such a huge impact on the entire AI coding ecosystem.

Yet many developers still choose to ignore them and just keep prompting — unaware of the massive transformative effects this could have on their workflow.

Skills have been as game-changing as the Model Context Protocol (MCP) — and these two form a deadly combo when put together.

MCP lets your agents do real things, Skills teach them how to do those thing — in a much more sophisticated way than normal prompting.

Instead of stuffing thousands of lines of instructions or coding standards into every conversation, Skills package instructions, workflows, scripts, and supporting resources into reusable modules that Claude loads only when they’re relevant.

That makes them more context-efficient, more reliable, and far more capable than traditional prompt engineering.

Here are 5 incredible things that make Skills so essential in every serious developer’s daily workflow.

1. Intelligent progressive disclosure

Claude Skills uses a clever three-level loading system to choose the exact skill to deploy at the right moment.

• Level 1: Claude always loads a tiny YAML metadata header describing each skill.
• Level 2: If your request matches that metadata, Claude loads the skill’s SKILL.md instructions.
• Level 3: Large reference docs, examples, and edge-case documentation stay in separate files that Claude only opens if a workflow explicitly requires them.

Context window bloat has always been one of the biggest limitations of LLMs.

If you preload every workflow into every conversation, you quickly waste tokens and dilute the model’s attention.

Instead of carrying every playbook into every prompt, Claude expands only the knowledge needed for the current task.

2. Skills can run in multiple invocation modes

Claude Skills gives you two different invocation modes.

Passive Context runs automatically in the background. For example, a TypeScript Conventions skill can ensure Claude never uses any, always prefers interfaces, and follows your team’s coding standards whenever it writes code.

Direct Actions are explicitly invoked workflows. Commands like /commit or /review-pr trigger structured, repeatable processes without requiring you to rewrite the same instructions every time.

This gives you both invisible guardrails and reusable automation.

3. They’re more than prompts — they execute code

Traditional prompts rely entirely on probabilistic reasoning.

Claude Skills can also include executable Python, Bash, or other local scripts, allowing them to combine AI reasoning with deterministic execution.

For example, a skill can generate code, run a compiler or linter, validate a database schema, execute tests, and feed those verified results back into Claude before continuing.

Instead of guessing whether something works, the workflow can prove it.

4. Skills can learn from your codebase

Advanced skills often maintain a persistent learnings.md file.

As Claude encounters unusual bugs, failed approaches, or project-specific edge cases, it records what it learned. Future executions can read those notes before solving similar problems.

Rather than remaining static instructions, Skills gradually adapt to the realities of your codebase over time.

5. Skills can work together

Skills aren’t limited to acting in isolation.

A complex request might trigger a Research Skill, which hands its findings to an Architecture Skill, followed by a Testing Skill that generates unit and integration tests.

Some advanced workflows can even spawn sandboxed subagents to perform independent tasks — like gathering database metrics via MCP — before reporting the results back to the primary workflow.

Instead of one giant prompt trying to do everything, specialized skills collaborate to solve increasingly complex engineering tasks.

Claude Code Skills symbolized a major transition from prompt engineering to capability engineering.

Progressive loading keeps context efficient, passive and direct invocation make workflows flexible, executable scripts provide deterministic reliability, persistent learnings help skills evolve with your project, and composable skills enable sophisticated agentic workflows.

The result isn’t just a smarter prompt — it’s an intelligent, reusable system that continuously expands what Claude can do.

Things just got even wilder with this incredible AI design tool.

Google just made it 10 times more powerful than it already was — it’s just terrifying now what it can do.

We are no longer just talking about turning one or two text prompts and sketches into UI mockups and front-end code.

The old Google Stitch: pretty awesome, but still too short-sighted and primitive:

Now Google Stitch wants to totally eradicate web designers taking over the entire process of designing an app — from idea-start to code-finish.

The new Google Stitch: full-fledged design engineer:

The fact that it literally now has its own MCP servers to integrate with Claude Code and the rest tells you everything you need to know…

The focus is now on the entire design system, not just one or two cool screens.

• How it evolves and all the different directions it could take
• How cohesive and well-defined the design language is
• How seamlessly the design transfers to the live codebase

1) AI-native infinite canvas for multimodal design

The upgraded Stitch introduces a redesigned interface built around an infinite canvas where users can combine text, screenshots, sketches, references, and even code in one space.

Instead of relying on a single prompt, the canvas becomes the working context for the design agent.

You can:

• Drop UI inspiration images directly onto the canvas
• Add product requirements or notes beside layouts
• Paste existing components or code snippets
• Generate multiple UI directions side-by-side
• Iterate visually instead of sequentially

This turns Stitch into a visual thinking environment where ideas, references, and outputs live together.

2) Project-aware design agent

Stitch now includes a design agent that understands everything on the canvas and uses it as context for generating interfaces. The agent can interpret requirements, follow style direction, and evolve designs as the project grows.

Key capabilities:

• Generate full app flows from high-level descriptions
• Expand a single screen into a multi-screen product
• Modify layouts based on new instructions
• Maintain visual consistency across generated pages
• Create alternate design directions instantly

The agent works continuously with the canvas rather than responding to isolated prompts.

3) DESIGN.md for reusable design systems

A major addition is DESIGN.md, a structured file that stores design rules, branding, layout preferences, and component behavior. Stitch uses this file as a persistent source of truth when generating UI.

With DESIGN.md you can:

• Define typography, spacing, and color tokens
• Enforce brand consistency across screens
• Share design systems between projects
• Import design rules from external sources
• Export system logic for developers

This allows Stitch to generate interfaces that follow consistent design language automatically.

4) Instant interactive prototyping

Stitch can now transform generated layouts into working interactive prototypes. Instead of static screens, designs can simulate navigation, flows, and user interactions.

Capabilities include:

• Clickable navigation between generated screens
• Auto-generated user journeys
• Multi-screen flow simulation
• Interactive preview mode
• Logic-based next screen generation

This allows teams to validate product flows immediately after generating UI.

5) Voice-driven design and live critique

The upgrade introduces voice interaction directly inside Stitch. Users can speak instructions, request feedback, and iterate designs conversationally.

Examples:

• Ask Stitch to redesign a landing page verbally
• Request alternative layouts using voice
• Get live critique of UX decisions
• Ask the agent to improve hierarchy or spacing
• Iterate rapidly without typing

This makes the design workflow more fluid and conversational.

6) Higher-quality UI generation with improved model capabilities

The latest version improves layout reasoning, spacing, hierarchy, and multi-screen coherence. Stitch can now generate more structured and realistic interfaces across different product types.

Enhancements include:

• Better responsive layout structure
• Improved component consistency
• Stronger visual hierarchy
• More realistic product UI patterns
• Cleaner spacing and typography

These improvements make generated designs closer to production-ready outputs.

7) MCP server support for connected workflows

The upgrade also introduces MCP (Model Context Protocol) server support, allowing Stitch to connect to external tools, environments, and development workflows.

With MCP support, Stitch can:

• Connect to component libraries
• Access external design systems
• Interface with developer environments
• Pull context from connected tools
• Push generated UI into implementation workflows

This allows Stitch to function as part of a larger AI-powered product development pipeline rather than a standalone design tool.

Stitch at launch

• Prompt or image in
• UI screens out
• Chat-based refinement
• Theme adjustments
• Export to Figma or front-end code

Stitch after the recent major upgrade

• Infinite canvas for text, images, and code
• Persistent project-aware design agent
• Agent manager for parallel explorations
• DESIGN.md for reusable design rules
• Interactive prototyping and flow generation
• Voice-based critique and live edits
• MCP server integration for connected workflows
• Improved generation quality with newer models

That comparison shows the real story: Stitch has evolved from a fast UI generator into a more opinionated AI design environment.

The new Stitch is designed for a wider audience than traditional design tools usually target. It works for both professional designers exploring many variations and founders shaping a first product idea.

The practical implication is that Stitch now sits at an interesting intersection:

• for non-designers, it lowers the barrier to making presentable interfaces
• for designers, it speeds up ideation and branching
• for developers, it tightens the handoff from design intent to code and downstream tools

The strongest part of the upgrade is that these pieces reinforce each other. The infinite canvas creates richer context, the design agent uses that context, DESIGN.md stabilizes consistency, prototypes make ideas testable sooner, voice interaction reduces friction, and MCP integration connects everything to real development workflows.

Claude Code Ultraplan takes software development to a whole different level.

Many developers are still only interested in using AI to generate code and nothing else.

This might work fine for simple changes — but when it comes to the massive, complicated, high-value codebase changes that really make the most of AI coding? It’s a recipe for disaster.

That’s why Claude Code is packed with sophisticated features like Ultraplan — to cleanly separate the intensive planning and design process — from the actual code implementation.

Claude Code Ultraplan is a cloud-powered, multi-agent development workflow that unblocks your local terminal by generating and critically debating alternative architectural strategies in parallel, producing a hyper-specific, locked-down blueprint that completely eliminates AI drift so the code is generated flawlessly on the very first try.

And this is quite different from the normal Plan Mode in Claude Code.

Ultraplan doesn’t keep you locked insid`e a terminal — it moves architectural planning to the cloud and presents the results in an interactive browser workspace.

Enabling a workflow that seamlessly handles complex refactors, enormous migrations, and large-scale feature development.

Let’s check out 5 things that make Claude Code Ultraplan so essential in modern AI-powered development.

1. Blazing fast parallel cloud processing & exploration

Planning happens in parallel.

And unlike traditional AI planning that runs locally, Ultraplan performs its analysis in Anthropic’s cloud.

This lets Claude investigate multiple aspects of your project simultaneously with state-of-the-art models, and decide on the best possible plan of action.

For example, if you ask Claude to migrate an authentication system from sessions to JWT, it can explore dependencies, affected API endpoints, middleware, database changes, frontend updates, and migration risks in parallel before assembling a unified implementation strategy.

Because this work happens remotely, large architectural analyses complete much faster than if they had been done sequentially — while also producing a much more comprehensive implementation plan.

2. Review and modify plans intuitively like a pull request

The browser becomes your review workspace.

Rather than scrolling through hundreds of lines of text in a terminal, Ultraplan presents the proposed architecture in an interactive browser document.

You can leave inline comments on specific sections — like requesting an endpoint remain backwards-compatible — and Claude updates only the relevant portion of the plan instead of regenerating everything.

The interface also supports quick reactions for lightweight feedback and automatically generates an outline sidebar, making it easy to jump directly to database migrations, frontend changes, or infrastructure updates in large implementation plans.

The experience feels much closer to reviewing a design document or pull request than chatting with an AI in a CLI.

3. Your CLI stays free

Your terminal never gets blocked.

Since planning runs entirely in the cloud, your local terminal isn’t occupied while Claude analyzes the repository.

You can continue writing code, running tests, switching Git branches, or debugging while Ultraplan works in the background.

The CLI simply displays a planning status indicator until the architecture document is ready to review.

This eliminates one of the biggest workflow interruptions common with long-running AI coding sessions.

4. Choose where the actual code gets written

Once you’ve finalized the implementation plan, Ultraplan lets you decide where execution happens.

You can execute the plan entirely in Anthropic’s cloud, where Claude generates the implementation and prepares a structured GitHub pull request for review.

Alternatively, you can send the approved plan back to your local Claude Code session and execute the changes within your own development environment using your existing tools, credentials, and security policies.

This flexibility allows teams to balance cloud convenience with local control.

5. Built-in architecture diagrams

Large software migrations are difficult to understand from text alone.

Ultraplan addresses this by rendering live Mermaid diagrams directly alongside the implementation plan.

These visualizations can illustrate project structure, component dependencies, service interactions, and data flows before any code is modified, making it easier to validate architectural decisions and identify potential issues early.

A better way to build

Ultraplan combines cloud-scale analysis, collaborative browser reviews, visual architecture diagrams, and flexible execution options to help developers think through complex changes before implementation begins.

For teams working on enterprise applications, major migrations, or large refactors, this planning-first approach will prove just as valuable as the code Claude eventually writes.

Claude Code Dynamic Workflows will change the way developers code with AI forever.

It fixes all the problems we’ve had to deal with when trying to use the same agent to do everything — or when trying to make multiple agents work together.

It combines hard-coded code and dynamic LLMs in a way we’ve never seen before — to get the absolute best of both worlds.

Dynamic Workflows allows Claude write its own JavaScript orchestration scripts on the fly and spin up tens to hundreds of parallel subagents to tackle giant tasks end-to-end.

It doesn’t just spawn sub-agents randomly — it deploys them in incredible structured and sophisticated ways.

And the sub-agents are not just doing simple divide-and-conquer code generation like we’ve been seeing.

They are intelligently combining themselves in a way that’s perfectly customized to whatever massive task we give them.

Some of them might aggressively review the code another agent generated — some might be there to pick the best code generated among a set of other agents.

Let’s dive deeper into Dynamic Workflows and see all the ways it can level up the workflows of so many developers.

1. Fixes all the biggest failures of single-context LLMs

Traditional AI agents operate inside a single conversation, which creates three major failure modes.

Defeating agentic laziness

Give one agent a task like reviewing 50 files for a security issue and it eventually becomes difficult to track progress.

Tasks get summarized, details are missed, and the model may prematurely declare success.

Dynamic Workflows solve this through decomposition. Instead of one agent handling 50 files, Claude launches 50 focused subagents. Each agent sees only one file and one objective, while the orchestration layer manages the larger task.

Crushing self-preferential bias

In traditional workflows the same model often generates and reviews its own work.

Dynamic Workflows separate these responsibilities. One agent builds, another reviews, and a third may challenge both. Because reviewers operate in independent contexts, they evaluate outputs against the requirements rather than defending previous reasoning.

Preventing goal drift

Long conversations cause important constraints to disappear over time.

Requirements like maintaining backwards compatibility or preserving performance gradually get compressed out of context.

Dynamic Workflows keep agent conversations short and purpose-built, ensuring every worker remains tightly aligned with the original objective.

2. JavaScript-powered deterministic decomposition

This is one of the biggest breakthroughs with Dynamic Workflows.

Claude doesn’t just spawn agents on the fly — it first writes a custom orchestration program.

LLM’s are great but they can be unpredictable — and there is no guarantee on how they tackle a huge goal.

With this JavaScript orchestration layer, the workflow’s structural control flow is 100% deterministic and predictable.

The file calls multiple JavaScript functions, like agent():

• agent(prompt, options): Spawns an isolated subagent. Can enforce structured JSON data using a schema

We know exactly what is going to happen at the high-level — while letting the individual sub-agents decide how they go about the lower-level tasks distributed among them.

Instead of thinking and generating everything on the fly, Claude generates a JavaScript workflow that determines:

• How work is divided
• Which agents are created
• How outputs are verified
• How failures are handled
• How results are synthesized

Because these workflows are standard artifacts, developers can save, modify, version-control, and rerun them. Over time, teams can build libraries of reusable workflow harnesses and custom agent networks tailored to their engineering processes.

3. Advanced multi-agent design patterns

Dynamic Workflows can combine sophisticated agent architectures on demand.

Tournaments

Multiple agents attempt the same problem using different approaches. A separate judging agent evaluates the candidates and selects the strongest solution.

Adversarial verification

A dedicated reviewer is created with one goal: reject the output.

This agent actively searches for security vulnerabilities, logic flaws, hidden assumptions, and edge cases, creating a far more rigorous review process than self-evaluation.

Fan-out-and-synthesize

For large-scale tasks, Claude distributes work across dozens or hundreds of parallel agents operating independently. Once completed, the workflow pauses and synthesizes all outputs into a unified result.

This pattern is ideal for migrations, audits, documentation generation, and repository-wide code changes.

4. Powering massive engineering projects

The scale enabled by Dynamic Workflows is difficult to overstate.

It’s really hard to overstate the sort of massive scale you could get from using Dynamic Workflows.

A prominent example is the Bun runtime migration from Zig to Rust. Using workflow-driven orchestration, approximately 750,000 lines of Rust code were generated while maintaining roughly 99.8% compatibility with the existing test suite.

Projects that would traditionally span multiple quarters can increasingly become automated jobs running in the background for hours or days.

5. Built-in resumability and safety

Long-running workflows require operational reliability.

The runtime continuously tracks workflow state and subagent progress. If a crash or disconnection occurs, execution resumes from the last checkpoint rather than restarting from scratch.

To prevent runaway costs, the system also provides visibility into workflow execution, token consumption, and resource usage, while enforcing limits that stop recursive workflows from spiraling out of control.

Dynamic Workflows represent a shift from prompt engineering to orchestration engineering.

Rather than asking a single AI agent to solve increasingly complex problems, Claude generates a custom execution harness, launches specialized workers, verifies their outputs, tracks state, and synthesizes results.

The future of AI development is not single super-agent.

It’s dynamically generated teams of specialized agents coordinated by software the AI writes for itself.

Claude Code’s Plan Mode is so underrated and underused.

Many developers are just prompting and prompting — they don’t realize how much this absolute beast of feature would take their entire Claude Code experience to the next level.

Plan Mode completely transforms Claude into a different kind of agent with a totally different objective.

Rather than immediately writing code, Claude shifts into a specialized, read-only, planning workflow designed to analyze, design, review, and document a solution before a single file is even touched —

Dramatically improving decision making, context efficiency, and implementation in the most complex and convoluted projects.

1. Plan Mode completely changes Claude’s way of thinking

When Plan Mode is enabled, Claude receives a different set of system instructions that fundamentally change how it approaches problems.

Instead of optimizing for just code generation, it optimizes for planning in four distinct phases.

• Initial Understanding: Explore the codebase and identify relevant files and dependencies.
• Design: Evaluate implementation approaches and trade-offs.
• Review: Validate the proposed design against the realities of the codebase.
• Final Plan: Produce a concise roadmap explaining what should change and why.

It turns into a sophisticated system designer.

2. Genius solution to processing massive repositories

Going through all the files in a large codebase can consume a huge amount of context and tokens.

To avoid this Claude Code uses a specialized, lightweight “Explore” subagent (like Claude Haiku) during the discovery phase.

This lightweight agent is responsible for searching, indexing, and reading files before passing only the most relevant findings back to the primary model.

Instead of stuffing thousands of lines of code into the main context window, the Explore subagent filters out noise and returns only the information needed for planning.

This allows the primary model to spend its context budget on reasoning rather than file exploration.

3. Guaranteed immutability

Another important detail is that Plan Mode is not activated through prompting alone.

When you switch into Plan Mode, Claude’s ability to modify the codebase is restricted by the underlying CLI harness itself. Write operations, file edits, and other mutating actions are removed from its available toolset.

In other words, Claude isn’t simply being asked not to make changes — it literally cannot make them.

This creates a genuinely safe environment for:

• Architecture reviews
• Migration planning
• Security audits
• Refactoring analysis
• Large-scale code exploration

There’s no risk of an accidental file modification while you’re still evaluating options.

4. The plan is a persistent, tangible artifact

The plan doesn’t exist only in the conversation.

Behind the scenes, Claude generates a markdown-based planning artifact that can persist beyond the immediate session. The plan becomes something tangible rather than a temporary stream of thoughts.

You can open it up in your own editor, tweak Claude’s planned steps yourself, and then feed that modified markdown file back into the tool to execute.

It turns planning into a deliverable, which is really powerful.

Instead of reviewing ideas scattered throughout a chat, developers can work from a structured roadmap that can be revised, shared, and reused.

5. Plans can evolve into the engine of the entire project

Many users initially adopt Plan Mode to make coding safer.

Over time, they discover it’s just as useful for documentation.

Because plans are structured markdown artifacts, they naturally evolve into:

• Feature roadmaps
• Migration checklists
• Implementation specifications
• Architecture reviews
• Onboarding documents

In many cases, the plan itself becomes the most valuable output. Even if implementation never happens, the document captures a deep understanding of the system and a clear strategy for future work.

The real purpose of Plan Mode isn’t preventing Claude from writing code.

It’s separating thinking from doing.

By combining a planning-focused workflow, a dedicated exploration subagent, hard platform-level restrictions, and persistent planning artifacts, Claude Code creates an environment where analysis comes before execution — which ends up dramatically improving the quality of the execution.

CLAUDE.md is so much more than most developers realize.

It’s supposed to be a special file for providing persistent instructions and context to Claude Code.

But many developers think they can just dump whatever they like inside it — some feel it’s just a fancy way to avoid writing the same prompts over and over.

Some don’t even ever use it — missing out on one of the most powerful ways to shape how Claude behaves inside a project.

The reality is that CLAUDE.md is far more nuanced than it appears. Understanding a few non-obvious facts about it can completely change how you work with Claude Code, and the results it produces for you.

Here are five things every developer should know about CLAUDE.md.

1. It’s not a README or project summary

Actually not a very good CLAUDE.md:

A README exists to explain a project.

A CLAUDE.md exists to guide an agent working inside that project.

The most effective files don’t try to document everything. Instead they focus on specific expectations, workflows, constraints, and preferred ways of working.

For example:

• Particular project-specific commands to run — or NOT run
• What must happen before code is merged
• Which patterns should be avoided when responding to prompts
• How tasks should be approached

Thinking of CLAUDE.md as documentation often leads to bloated files. Thinking of it as a behavioral contract usually leads to better results.

2. It’s not a system prompt

One common misconception is that CLAUDE.md functions like a system prompt.

It doesn’t.

Claude Code loads the contents of the file into the model’s normal user context — it’s treated as important guidance rather than an untouchable source of truth.

It’s not an unbreakable instruction layer that reigns over every other context.

This is why clear, specific instructions tend to work much better than vague recommendations.

3. There can be more than one CLAUDE.md

Many developers assume there is a single CLAUDE.md at the root of a project.

But actually Claude Code supports a layered memory system.

Instructions can exist at multiple levels and be combined as Claude works through different parts of a codebase.

This makes CLAUDE.md less like a project configuration file and more like a hierarchy of contextual instructions that shapes behavior in different situations.

4. Claude can automatically generate your first CLAUDE.md

A surprising number of developers still start from a blank file.

Claude Code includes an /init command that can inspect a repository and generate an initial CLAUDE.md automatically.

The generated file won’t be perfect, but it provides a useful starting point and can save time by capturing common project information that would otherwise need to be written manually.

For many teams, it’s the fastest way to establish a baseline memory file.

5. CLAUDE.md is only one layer of Claude’s massive memory system

Perhaps the most important thing to know is that CLAUDE.md is not Claude Code’s entire memory system.

It’s just one layer.

Claude can draw context from user-level memory, project-level memory, session context and many other memory sources.

So when Claude successfully remembers how your team works, that behavior is often influenced by more than a single file.

The most interesting thing about CLAUDE.md is that it represents a new way of programming behavior.

Traditional software is configured through code and settings. Claude Code introduces the idea of configuring an agent through natural language instructions.

That’s why two developers can use the same model and get very different outcomes. The difference isn’t the model — it’s the behavior defined by the memory and instructions surrounding it.

Anthropic just shocked the world — they just released the most powerful AI model in human history.

This is Claude Fable 5 — the public-facing version of the monstrous Claude Mythos model that they’re hiding from the public.

There is no contest — this thing completely decimated all the benchmarks out there — Opus 4.8 looks like a toy compared to what this thing can do:

• Dominated several AI model leaderboards within hours of launch
• Migrated a major tech company’s codebase of 50 million lines in less a day — previously took several months of work
• Better than any other model at coding & software engineering

It’s been absolutely wild — People are casually one-shotting entire games, 3D worlds, full-stack apps, and code optimizations that shouldn’t be possible, but here we are.

And to think that this is actually a restricted version?

Imagine having a model so powerful that you literally cant release it to the world without disastrous consequences.

1. The Fable vs. Mythos story: A tale of two twins

It’s definitely one of the most fascinating aspects of this incredible launch.

The Claude Fable 5 and Claude Mythos 5 relationship.

Both models share the same underlying architecture and core capabilities.

The main difference isn’t intelligence — it’s access.

Claude Fable 5

• Released to the general public
• Full reasoning, coding, and agentic capabilities
• Protected by real-time safety classifiers
• Automatically restricted in high-risk domains

Claude Mythos 5

• The unrestricted, god-mode version of the same model
• Available ONLY to vetted organizations
• Accessed through Anthropic’s Project Glasswing program
• Designed for advanced cybersecurity and research applications

Anthropic itself has emphasized that the naming distinction reflects safety boundaries rather than capability differences.

In other words, Fable isn’t weaker model. It’s a safer one.

A pair of identical twins — one allowed to walk freely among the public.

The other considered so dangerous that its access is limited to people responsible for defending critical digital infrastructure.

That alone makes this launch unlike anything we’ve seen before in AI.

2. Absolutely decimates benchmarks

Fable 5’s benchmark results are among the most impressive ever reported for a public AI model.

On Humanity’s Last Exam, one of the toughest AI reasoning tests in existence, Fable scored 64.5% with tools, comfortably outperforming GPT-5.5 (41.4%) and Gemini 3.1 Pro (44.4%).

Its dominance extends to software engineering. On SWE-Bench Pro, the industry’s leading coding benchmark, Fable achieved 80.3%, compared to GPT-5.5’s measly 58.6%.

The model also posted leading scores across computer use, spatial reasoning, cybersecurity, and terminal-based tasks, establishing itself as one of the strongest general-purpose AI systems ever released.

3. Unbelievable long-horizon coding ability

Fable 5 is specially built for massive, complicated tasks that unfold over hours — or even days.

The model can:

• Explore its environment independently
• Build and execute its own plans
• Launch parallel sub-agents
• Monitor its own progress
• Self-correct when things go wrong

The most shocking example comes from Stripe.

The Stripe test

Fable was tasked with working inside a 50-million-line Ruby codebase and completed a codebase-wide migration in a single day—compressing what would normally require months of engineering effort into hours.

It’s just absolutely shocking how crazy things have gotten.

But this is what Anthropic means by long-horizon intelligence: an AI capable of staying focused on complex objectives for hours, not just seconds.

4. Sophisticated real-time Opus hand-off

One of Fable’s most innovative features is something users may never notice.

When the model encounters sensitive areas such as:

• Advanced cybersecurity
• Biochemical research
• Model distillation
• Other high-risk domains

…it doesn’t simply refuse.

Instead, Anthropic’s safety systems automatically hand the conversation off to Claude Opus 4.8, which completes the task under stricter safety constraints.

According to Anthropic:

• Roughly 95% of user sessions run entirely on Fable 5
• Only a small fraction trigger the Opus fallback
• Users receive a seamless experience rather than a hard refusal

It’s a novel approach to AI safety: switch models instead of shutting the conversation down.

5. Why the main Mythos simply had to be locked up

If it’s the same model, why not release it publicly?

Cybersecurity.

Serious cybersecurity concerns.

Anthropic has unambiguously described Mythos 5 as “possessing unprecedented offensive and defensive cyber capabilities”.

Its performance on security-focused benchmarks significantly exceeds previous generations — Mythos-class systems have reportedly helped identify thousands of critical vulnerabilities.

It even uncovered a 27-year-old security flaw in OpenBSD and wrote a remote code execution exploit for a 17-year-old bug in FreeBSD completely on its own.

It is so effective at finding bugs that open-source maintainers literally begged Anthropic to slow down its disclosures because human developers couldn’t write the patches fast enough.

For Anthropic, the risk-reward equation was clear:

• Release a safeguarded version to the public
• Restrict the unrestricted version to trusted partners
• Maintain oversight of the model’s most powerful capabilities

This model is going to completely rip apart everything we thought was possible with AI models and AI-powered software development.