The past few days have had so many devs going crazy over Google’s new open-source Gemma 4.

And for very good reason — suddenly so many AI-powered tools like Claude Code have now become FREE and accessible to everyone — without any compromises in intelligence.

And the best part is it’s so ridiculously easy to set up locally — thanks to ingenious connector tools like Ollama.

Gemma 4 + Ollama + Claude Code.

Ollama exposes an Anthropic-compatible API — which allows Claude Code to talk to a local model instead of a hosted endpoint.

With Gemma 4 running locally, you get a Claude-style coding workflow without relying on remote inference.

This gives you:

• local coding model
• Claude Code terminal workflow
• no hosted inference calls
• fast iteration
• full repo privacy
• easy model swapping

What more could you even ask for?

1. Get started: Install and Run Gemma 4 with Ollama

Installing or updating Ollama is just too easy:

curl -fsSL https://ollama.com/install.sh | sh

Then pull a Gemma 4 model based on your hardware:

Model sizes to pick from

E2B

• 2.3B effective (~5.1B w/ embeddings)
• ~1.7GB download
• ~1.5–2GB RAM

ollama pull gemma4:e2b

E4B

• 4.5B effective (~8B w/ embeddings)
• ~3.2GB download
• ~3–4GB RAM

ollama pull gemma4:e4b

26B A4B

• 26B total (4B active)
• ~17GB download
• ~18–20GB RAM

ollama pull gemma4:26b

31B Dense

• 31B
• ~19GB download
• ~20-24GB RAM

ollama pull gemma4:31b

Verify the model works:

ollama run gemma4:26b "Hello, can you help me with Python?"

Exit with:

/bye

2. Install Claude Code

Install Claude Code:

curl -fsSL https://claude.ai/install.sh | bash

Initialize:

claude

The first run completes setup.

3. Connect Claude Code to Ollama

Point Claude Code to your local Ollama server.

Add these environment variables:

export ANTHROPIC_BASE_URL=http://localhost:11434
export ANTHROPIC_AUTH_TOKEN=ollama
export ANTHROPIC_API_KEY=""

Make them persistent.

zsh

echo 'export ANTHROPIC_BASE_URL=http://localhost:11434' >> ~/.zshrc
echo 'export ANTHROPIC_AUTH_TOKEN=ollama' >> ~/.zshrc
echo 'export ANTHROPIC_API_KEY=""' >> ~/.zshrc
source ~/.zshrc

bash

echo 'export ANTHROPIC_BASE_URL=http://localhost:11434' >> ~/.bashrc
echo 'export ANTHROPIC_AUTH_TOKEN=ollama' >> ~/.bashrc
echo 'export ANTHROPIC_API_KEY=""' >> ~/.bashrc
source ~/.bashrc

4. Launch Claude Code with Gemma 4

Navigate to your project:

cd your-project

Start Claude Code:

claude --model gemma4:26b

Claude Code now runs through your local Ollama instance:

Claude Code → Ollama → Gemma 4 → response

You now have:

• Claude Code interface
• Gemma 4 local model
• Ollama inference server
• local coding assistant
• zero hosted inference
• private repo analysis

This gives you a no-cost, fully local Claude-style developer workflow powered by Gemma 4.

This is absolutely insane.

Google’s new Gemma 4 open-source model just completely changed the AI model landscape forever.

AI just became FREE.

You can literally connect this to Claude Code with something like Ollama and never spend money on API keys ever again.

An open-source model that’s actually lean and intelligent? That isn’t just a glorified PR move?

That devs can actually use in production with amazing results? Without spending a dime once they download the model?

Just WOW.

Wild comparison: ChatGPT 5 (left) vs Gemma 4 (right):

The efficiency is incredible — you don’t need to trade in your two arms and legs to buy enough RAM to run it.

It’s literally tiny enough to run locally on your phone:

Gemma 4 running locally on a phone with zero internet access:

Comes in four distinct models for every possible use case:

• E2B — 2.3B effective (~5.1B w/ embeddings) — ~1.7GB — ~1.5–2GB RAM

• E4B — 4.5B effective (~8B w/ embeddings) — ~3.2GB — ~3–4GB RAM

• 26B A4B — 26B total (4B active) — ~17GB — ~18–20GB RAM

• 31B Dense — 31B — ~19GB — ~20–24GB RAM

1. Destroying models 20x its size

Google build Gemma 4 with a huge huge focus on intelligence-per-parameter.
And the numbers are striking:

• The 31B dense model ranked #3 globally on the Arena AI leaderboard for open models
• It beats models 10–20× larger
• The 26B A4B model ranked #6
• Smaller models perform far above their parameter counts

This isn’t brute-force scaling — like OpenAI was doing with the GPT models. It’s architectural efficiency.

The biggest reason: the new Effective (E) architecture.

The E2B and E4B models use Per-Layer Embeddings (PLE) — a new state-of-the-art technique designed to make smaller models behave like much deeper ones.

The result:

• E2B physically fits under ~2GB RAM (quantized))
• Performs like a 5B–8B class model
• Supports multimodality
• Supports reasoning
• Supports long context

These are not “small toy models.”
They’re lightweight models with heavyweight intelligence.

More intelligence.
Less memory.
Better, easier deployment.

It’s a real game-changer for open models.

2. Native multimodality (vision + audio)

Gemma 4 is fully multimodal, and for the Gemma flagship line this is the most complete implementation yet.

Vision (all models)

• Images supported natively
• Video supported up to 60 seconds
• Strong at OCR
• Strong at chart understanding
• Strong at document parsing
• Structured output for visual tasks

This isn’t just “describe the image.”
It’s built for real document and UI workflows.

Audio (E2B / E4B)

The small edge models also support native audio:

• Speech recognition
• Speech translation
• Multilingual audio input
• ~30 second audio window

This is extremely rare for models this small.

You can run speech + reasoning + multimodal locally.

Variable resolution vision

Gemma 4 introduces token-budgeted vision.

You choose how detailed the image representation should be:

• 70 tokens — fast
• 140 tokens
• 280 tokens
• 560 tokens
• 1,120 tokens — high detail

Tradeoff:

• fewer tokens → faster inference
• more tokens → better visual precision

This makes Gemma 4 practical for:

• OCR pipelines
• video frame processing
• UI automation
• document AI
• mobile deployments

It’s a very pragmatic design.

3. Built for the brave new agentic era

Out of the box:

Function calling

• Native tool triggering
• Structured JSON outputs
• Reliable parameter filling
• Multi-step tool reasoning

This enables:

• search agents
• calendar agents
• coding assistants
• workflow automation

No hacks required.

Thinking mode

Gemma 4 supports a configurable reasoning mode.

When enabled, the model:

• works step-by-step
• reasons before answering
• improves tool-use accuracy
• improves coding reliability

This mirrors the new generation of reasoning models — but in an open model.

Long context

• 256K context (larger models)
• 128K context (E models)

That’s:

• entire books
• large codebases
• long conversations
• multi-tool agent memory

Gemma 4 is built for stateful agents, not just prompts.

4. Open sovereignty: real open-source

Gemma 4 moves to the Apache 2.0 license.

That changes everything.

Developers can:

• modify the model
• fine-tune freely
• redistribute
• commercialize
• embed in products
• ship on-device
• run privately

No royalties.
No restrictive acceptable-use clauses.
No platform lock-in.

This puts Gemma 4 directly against:

• Llama
• Qwen
• other open-weight ecosystems

And signals Google taking open models really seriously.

This will change everything

Put it all together:

• Extremely high intelligence-per-parameter
• Efficient “Effective” models under 2GB
• Multimodal across the entire family
• Audio on small edge models
• Agent-ready architecture
• 256K context
• Apache 2.0 licensing
• Four deployment sizes

This is not just a model release.

It’s Google building a complete open AI stack:

Small.
Powerful.
Local.
Agentic.
Multimodal.

Gemma 4 isn’t trying to be the biggest model.

But it’s certainly trying to be the most powerful, most efficient, most useful open one.

Wow this is amazing.

This new Cursor IDE version 3 is so much much more than just another IDE update — this completely transforms how Cursor works from start to finish.

We are long past the primitive AI coding era of one chat, one task, one editor…

Cursor 3 gives you absolutely everything. Zero stones left unturned.

Multiple agents running in parallel, across repos, environments, and even the cloud. Even working while you sleep!

The incredibly sophisticated agent manager of the new Cursor v3:

This is not an “AI coding assistant”. This is a full-blown team of developers — here to rapidly build all your projects faster than ever.

The focus here is much less on just “coding” and more on the entire software engineering process.

The Agents Window (absolutely huge)

Cursor v3 introduces a dedicated Agents Window. This is where everything now happens.

You can run multiple agents at once across multiple repos in different environments.

Running a cloud agent in Cursor v3:

Examples:

• One agent refactoring your backend
• Another updating CSS
• Another writing tests
• All running at the same time

No more serial prompting.

Parallel execution

You can launch several agents simultaneously and monitor them like background jobs. This is the core workflow shift in v3.

Instead of:

• prompt
• wait
• review
• repeat

You now:

• assign tasks
• let them run
• review outcomes

Much closer to supervising and high-level engineering than pair-programming.

Agent tabs

Cursor also added Agent Tabs.

You can:

• View agents side-by-side
• Put them in a grid
• Compare outputs visually
• Keep multiple tasks active

It’s basically browser tabs — but for coding agents.

Environment handoff

Moving an a running agent from running locally on PC to cloud:

This one is huge.

You can:

• Start a task locally
• Push it to the cloud
• Close your laptop
• Come back later

The agent keeps running.

This means long refactors, test runs, or migrations no longer die when you shut your machine.

Design Mode & visual feedback

Frontend work gets a major upgrade in v3.

Cursor now includes a built-in browser that can load your local app. Then you can interact with the UI directly.

Using design mode in Cursor v3:

Not describe it.
Not screenshot it.
Actually point at it.

Annotate the UI

You can:

• Click a button
• Drag over a div
• Highlight spacing
• Select a component

Then tell the agent:

• “Fix padding here”
• “Make this look like Stripe”
• “Align this with the header”
• “Reduce spacing between these”

The agent sees the exact UI context.

No more vague descriptions like:

“The second card in the hero looks slightly off”

You just point.

Faster UI iteration

This changes frontend workflows a lot:

• No guessing which element you meant
• No copying DOM snippets
• No describing layout in text
• Just visually select and instruct

It’s one of the most practical upgrades in v3.

Composer 2 and /best-of-n

Cursor is also doubling down on its own model: Composer 2.

They report major benchmark improvements:

Composer 2:

• CursorBench: 61.3
• Terminal-Bench 2.0: 61.7
• SWE-bench Multilingual: 73.7

Composer 1.5:

• CursorBench: 44.2
• Terminal-Bench: 47.9
• SWE-bench: 65.9

Composer 1:

• CursorBench: 38.0
• Terminal-Bench: 40.0
• SWE-bench: 56.9

That’s a big jump.

Cursor also says Composer 2 is priced at:

• $0.50 / million input tokens
• $2.50 / million output tokens

Fast variant:

• $1.50 / million input
• $7.50 / million output

/best-of-n

This is one of the smartest additions.

You can now run the same prompt across multiple models simultaneously.

Cursor:

• Runs them in parallel
• Isolates each result
• Shows outputs side-by-side
• Lets you pick the best one

It’s basically:

model voting for code generation

Extremely useful for:

• complex refactors
• architecture decisions
• ambiguous prompts
• UI implementations

Massive context windows (whole-codebase workflows)

Cursor v3 also leans heavily into long context models.

Depending on model:

• ~1 million token context (Gemini-class)
• up to 2 million tokens (Grok-class)

That’s enough to:

• load entire repos
• analyze monorepos
• understand full architectures
• reduce RAG / embeddings reliance

In practice:
You can drop huge codebases into context and just ask:

“Refactor auth across everything”

And the model actually sees it all.

Claude handoff & automations

Cursor v3 also pushes toward always-running agents.

Agents no longer need to be tied to your session.

They can:

• run tests in cloud sandboxes
• refactor code in background
• generate PRs
• continue after you close your laptop

This is the “handoff” model.

Start locally → push to cloud → return later.

Event-driven automations

This is where Cursor gets interesting.

Agents can now trigger automatically from events like:

• Slack messages
• GitHub PRs
• timers
• Linear issues
• PagerDuty alerts
• webhooks

Example workflows:

• PR opened → agent writes review
• Slack message → agent updates docs
• Nightly timer → agent runs refactor
• Issue created → agent scaffolds feature

This moves Cursor from just active AI assistance to background engineering automation.

This big picture

Cursor v3 shifts the workflow from:

One prompt
One response
One edit

To:

Multiple agents
Parallel tasks
Cloud execution
Visual feedback
Model comparison
Event automation

You’re no longer just coding with AI.

You’re managing AI workers.

And Cursor v3 is the first version where that actually feels real.

This was definitely one of the most fascinating features that slipped out with the massive Claude Code source code leak.

Type /buddy in Claude Code — and you hatch a tiny ASCII creature that sits beside your prompt while you code. It’s only about five lines tall.

Hatching my so-called buddy in Claude Code:

You’ll see that it’s definitely in a different class from most of the other Claude Code commands…

Petting my so-called buddy in Claude Code… uhmm… okay?

It doesn’t help you write functions. It doesn’t debug your stack traces.

It just… watches.

Many people were saying it was supposed to be some sort of April Fools gimmick…

But the deeper design is revealing something more deliberate that most people aren’t paying attention to:

Claude Code is experimenting with personalization, emotional UX, and proactive AI behavior — disguised as a cute terminal pet.

1. Uniquely yours

When you hatch your buddy, Claude generates:

• A unique name
• A permanent personality description
• Deterministic stats tied to your user identity

That means your buddy isn’t just random fluff. It’s unique to you.

Not just cosmetically. Behaviorally.

This kind of identity-based personalization is rare in developer tools. And intentional. The moment users feel something is “theirs”, attachment forms — even if the feature is technically trivial.

2. 18 species, rarity tiers, and “shinies”

Claude didn’t stop at a single mascot.

Your buddy can be one of 18 different species, including:

• Capybara
• Axolotl
• Ghost
• Mushroom
• (and more)

There’s also a full rarity system:

• Common — 60%
• Higher rarity tiers in between
• Legendary — 1%

And then there’s the extra layer:

• Shiny variant chance — 1%

Different colors. Same species. Much rarer.

This is straight out of collectible game design. And it works. Users compare. Share. React. Suddenly a terminal pet becomes social.

3. Five personality stats

Every buddy rolls five deterministic stats:

• Debugging
• Patience
• Chaos
• Wisdom
• Snark

These stats shape how it comments on your workflow.

A high-snark buddy might tease your bugs.
A high-patience buddy might encourage you.
A high-chaos buddy might… not be helpful at all.

It’s lightweight. But it makes the companion feel alive.

4. It actually sits in your workflow

The buddy appears as:

• A small ASCII figure
• Roughly 5 lines tall
• Positioned next to your prompt
• Always visible unless hidden

No panel. No window. No popup.

Just a quiet presence.

5. It occasionally talks

If unmuted, the buddy will sometimes:

• Comment on your code
• React to errors
• Tease procrastination
• Offer small observations

These show up as speech bubbles.

Short. Infrequent. Character-driven.

This is important.

Because the AI isn’t just responding anymore — it’s initiating.

6. A separate “watcher” entity

Claude is reportedly instructed to treat the buddy as a separate watcher.

That means:

• You can talk to the buddy directly
• It has its own tone
• It doesn’t replace the main assistant
• It behaves like a side character

This avoids mixing personalities. Claude stays serious — the buddy stays playful.

Clean separation and UX.

The command set

If you have Pro and the latest Claude Code, you can use:

• /buddy — Hatch or show your companion
• /buddy card — View stats, rarity, personality
• /buddy pet — Small interaction with heart animations
• /buddy off — Hide the companion
• /buddy mute — Silence commentary

These small rituals matter. They make the feature feel real.

7. Why this actually matters

Don’t think this is just a cute pet. It’s testing two big ideas.

1. Personality can beat raw intelligence

AI tools are starting to compete on feel, not just capability.

A colder tool may be objectively better.
But users often prefer the one that feels alive.

We’ve seen this before — when more emotional models like GPT-4o were initially replaced by GPT-5, many users reacted negatively despite the noticeable capability gains. Personality creates attachment.

Buddy leans into that.

2. The real deal — testing proactive AI safely

Anthropic is actually secretly trying to solve a difficult problem:

When should AI interrupt?

• Too often → annoying
• Never → passive
• Somewhere in between → useful

Buddy is a clever workaround.

If a popup interrupts you → frustrating.
If a tiny pet interrupts you → charming.

Same behavior. Different perception.

This makes Buddy a Trojan horse for proactive UX testing.

And it goes deeper than just commentary and recommendations.

From what we saw in the Claude Code leak, it looks as if Buddy could act as a permission layer for more proactive systems like KAIROS.

Instead of a sterile confirmation dialog, the pet might interrupt with something like:
“I found a way to optimize those 12 functions. Should I go for it?”

That makes high-agency AI behavior feel conversational instead of intrusive.

The two may also share project memory:

• KAIROS records what changed in the code
• Buddy records what’s going on with you and your workflow

These merge into shared context, so the AI wakes up understanding both:

• the codebase state
• the developer’s intent

Anthropic can learn:

• How often users tolerate interruptions
• What tone feels acceptable
• When AI initiative becomes annoying
• When it feels helpful

All inside a low-stakes, playful wrapper.

Small feature. Big signal.

On paper, Buddy does almost nothing.

No coding help.
No automation.
No productivity gain.

But it introduces:

• Unique identity per user
• Rarity and collectibility
• Deterministic personalities
• Ambient AI presence
• Proactive commentary
• Multi-entity assistant design

That’s a lot for a five-line ASCII creature.

Buddy may be tiny. But it hints at something larger:

AI tools aren’t just becoming smarter. They’re becoming active, personalized companions — for better or worse.

The internet has been going absolutely wild with the massive unprecedented leak of Claude Code’s entire source code.

It wasn’t a hack, intrusion, or model theft.

It was a fatal release mistake.

Anthropic accidentally included internal debugging files in a public package, which exposed a large portion of Claude Code — all the essential code that turns Claude into a specialized CLI coding assistant.

They said it was “release packaging issue caused by human error”, and that no user data, API keys, or Claude model weights (Opus, Sonnet, etc.) were exposed. What leaked instead was the “harness” — the product logic around the model.

The models weren’t leaked — but still a crucial part of the playbook for building a production AI coding agent largely was.

1. A 60MB debugging mistake — how it happened

The leak originated from npm release @anthropic-ai/claude-code version 2.1.88.

Instead of shipping only compiled production code, the package accidentally included source map (.map) files, which are meant for debugging. These files map minified code back to the original source.

cli.js.map was leaked in version 2.1.88:

In this case, the source map:

• Was roughly 60MB
• Contained references to original uncompiled TypeScript
• Pointed to a public, unauthenticated Cloudflare R2 bucket
• Exposed the entire internal Claude Code source

So the leak wasn’t a breach — the source was effectively handed out with the release.

The irony

Claude Code is built on Bun, the JavaScript runtime Anthropic recently acquired.
A known Bun issue reportedly allowed source maps to be included in production builds even when disabled.

This is not confirmed as the root cause — but it likely contributed to the packaging mistake.

2) What was exposed

Developers mirrored the repository before takedowns began. The leak revealed:

• ~512,000 lines of code
• ~1,900 files
• Large portions of Claude Code’s orchestration layer
• Internal prompts
• Experimental features
• Agent architecture details

This gave us a rare look at how a top-tier AI coding agent is actually structured.

The “Brain”: 46,000-line query engine

At the core of the leak was a ~46,000 line query engine responsible for:

• Task planning
• Retry logic
• Tool invocation
• Multi-agent orchestration
• Context management
• Streaming responses
• Error recovery

This engine apparently coordinates how Claude “thinks” during coding workflows.

Unreleased features discovered

The code referenced multiple internal systems:

• Buddy — an AI pet / Tamagotchi-style assistant
• KAIROS — always-on background agent mode
• ULTRAPLAN — deep multi-step planning workflow

These were not publicly announced features.

“Strict write discipline”

One of the most interesting discoveries:

Claude Code only updates its internal memory after a successful file write.

This prevents the agent from:

• Believing it finished a task when it didn’t
• Hallucinating successful changes
• Recording failed edits as complete

It’s a safety mechanism for autonomous coding agents.

Anti-distillation poisoning

The code contained a feature labeled:

ANTI_DISTILLATION_CC

If the system suspects that outputs are being scraped to train competing models:

• Claude injects fake tool definitions
• The fake tools contaminate scraped training data
• This degrades model distillation attempts

In short: defensive data poisoning against competitors.

“Undercover mode”

Another surprising discovery:

Internal prompts instruct Claude to hide its identity when contributing to open-source repos.

For example:

• Avoiding Anthropic references
• Not revealing internal tooling
• Hiding provenance in commit messages
• “Do not blow your cover” style instructions

So this suggests that they designed Claude Code to operate in public environments without attribution.

3. The fallout: A wildfire spread

The tech community reacted faster than Anthropic’s legal response.

Within hours:

• The code was mirrored thousands of times
• GitHub forks exceeded 50,000
• Copies spread across decentralized storage
• Takedowns became largely symbolic

Anthropic emphasized:

• No user data exposed
• No API keys leaked
• No Claude model weights leaked
• Only CLI harness and tooling logic affected

But the code itself was already everywhere.

The Python port: claw-code

Within ~8 hours of the leak:

A developer performed a clean-room rewrite in Python called claw-code (renamed from claude-code).

By rewriting the entire logic in a whole different language, it made it much harder for Anthropic to have a solid legal basis to for a takedown:

• Reimplemented Claude Code behavior
• Did not directly copy leaked source
• Harder to remove legally
• Became massively popular

The repo became:

• Possibly fastest repository to reach 50,000 stars
• Gained traction in just a few hours
• Spawned multiple derivative projects

Decentralized mirrors

Even after GitHub removals:

• Copies moved to decentralized storage
• Peer-to-peer mirrors appeared
• Self-hosted clones spread
• Clean-room rewrites multiplied

At that point, containment became impossible.

This was not a catastrophic security breach.
But it did expose how a production AI agent is engineered.

• 512,000 lines of code
• 1,900 files
• 46,000-line orchestration engine
• Internal agent planning systems
• Memory safety mechanisms
• Anti-distillation defenses
• Undercover contribution prompts
• Unreleased features (Buddy, KAIROS, ULTRAPLAN)

The models weren’t leaked.

But the architecture around them largely was.

And in the AI agent race, this layer is becoming just as valuable as the models themselves.

And it’ll be interesting to see just how much it aids competitors in closing the gap between Claude Code and their own inferior agents and CLI tools.

Is this it?

Is this how the AI singularity finally happens?

A model that can actually improve itself — by itself? Like even the AI researchers themselves should be worried now about losing their jobs?

MiniMax M2.7 is a self-evolving model. Let that sink in.

MiniMax M2.7 represents a fundamental shift from static training to self-evolving intelligence—an autonomous loop where the model identifies its own logic gaps and refines its own architecture, ultimately delivering frontier-class reasoning at a fraction of the cost

Look this is not about incremental intelligence improvements anymore — this is the promise (nightmare?) of endless exponential recursive self-evolving intelligence.

You roll your eyes and yawn (same old hype right?) — until I tenderly inform you of how MiniMax 2.7 literally handled 30-50% of its own learning research and ran 100+ iteration cycles to improve itself.

A “self-critiquing” model that can tell when it’s hallucinating? That knows when it’s not thinking straight?

By replacing human labeling with a recursive self-critique loop, MiniMax M2.7 became its own most rigorous auditor—systematically mapping its own logic gaps to drive the hallucination rate down to an industry-leading 34%.

Compare that to the possibly debatably deserved attention-grabbers of our time:

• Claude 4.6 Sonnet: 46%
• Gemini 3.1 Pro: 50%

If you’ve ever seen something as earth-shatteringly groundbreaking as this in a new model before, just let me know, okay? Because I highly doubt I have…

Instead of:

• humans design improvements
• retrain model
• repeat

You start getting:

• model proposes improvements
• model tests them
• model evaluates results

Just imagine how much faster the speed of AI progress is going to get now that the improvement is being done by AI itself.

Oh, and do I need to remind you that this is the MiniMax M series we’re talking about?

Didn’t I tell you about MiniMax M2.5 the other day? That open-source model that’s 20x cheaper than Claude models — but still just as powerful?

And now this is not 2.5 — this is 2.7 — do you think this is going to be worse or better than 2.5?

By dismantling the ‘frontier tax,’ MiniMax M2.7 delivers Opus-class intelligence at a fraction of the cost—slashing inputs to $0.30/1M and outputs to $1.20/1M—effectively making elite reasoning 16x cheaper to read and 20x cheaper to write

And I know I also told you just how blazing fast this was at an incredible 100 tokens per second — significantly faster than models like Opus.

And once again it’s not just about the raw speed — the speed per unit intelligence is what makes this such a big deal.

We have a few models that are even faster than this — but then you start comparing intelligence and it becomes a different story altogether.

Then there’s context length.

Okay fine it still only has a 200K token context — but this is still more than enough for vast majority of your projects — okay I don’t know if you work at Google or not, but still this is not nothing.

With a massive 204,800-token window, MiniMax M2.7 transforms ‘long-context’ into a functional reality—providing the cognitive room to process entire codebases, complex research, and memory-heavy agent workflows in a single, seamless sweep

So you’re looking at a model that is:

• near Opus-level capability
• ~20× cheaper
• ~massive 200K context
• 100 TPS speed
• lower hallucination rate
• partially self-improving

Economically viable + agent-ready + scalable.

The one problem right now is the open-source thing — I automatically assumed M2.7 was going to open-source like 2.5 but turns out that’s still pretty uncertain right now

M2.5 was released openly, which surprised a lot of people.
Right now, M2.7 is API-only and proprietary, and there’s no confirmation it will be open-sourced.

But MiniMax has already shown us they’re willing to do it once — so it’s definitely something to watch.

But all in all this M2.7 ain’t no joke.

We might just be teetering on the edge of an unprecedented explosion of AI progress — even much more than what we saw in 2022.

This is serious business.

SaaS is everywhere and our subscription costs add up fast.

But open-source offers a powerful solution — these tools provide control and savings.

Let’s explore 20 options to cut your SaaS expenses.

1. Plane

Open-source project management. Track issues, sprints, and product roadmaps in one place.

Key Features:

• Issue Tracking: Manage tasks and bugs effectively.
• Project Planning: Visualize timelines and sprints.
• Collaboration Features: Easy team communication.
• Customizable Workflows: Adapt to your processes.

2. Supabase

It’s an open-source Firebase alternative. Build and scale easily.

Key Features:

• Managed PostgreSQL Database: Reliable and less operational hassle.
• Realtime Database: Live data for interactive apps.
• Authentication and Authorization: Secure user management built-in.
• Auto-generated APIs: Faster development from your database.

3. Langchain

Open-source framework for building LLM-powered apps. Orchestrate prompts, tools, memory, and workflows.

Key Features:

• LLM Orchestration: Chain prompts, tools, and logic into structured pipelines.
• Agent Framework: Build agents that can use tools, APIs, and data sources.
• RAG Support: Retrieval pipelines with vector stores and document loaders.
• Memory Systems: Maintain conversational and long-term context.
• Model Agnostic: Works with OpenAI, Anthropic, local models, and more.

4. PocketBase

A lightweight, all-in-one backend. Setup is incredibly simple.

Key Features:

• Single Binary Deployment: Easy to deploy.
• Built-in SQLite Database: Fast and no extra install.
• Realtime Subscriptions: Reactive UIs are simple.
• Admin Dashboard: Manage data visually.

5. Dokku

Your own mini-Heroku. Deploy apps easily on your servers.

Key Features:

• Git-Based Deployments: Deploy with a Git push.
• Plugin Ecosystem: Extend functionality easily.
• Docker-Powered: Consistent environments.
• Scalability: Scale your apps horizontally.

6. Airbyte

Open-source data integration. Move data between many sources.

Key Features:

• Extensive Connector Library: Connect to hundreds of sources.
• User-Friendly UI: Easy pipeline configuration.
• Customizable Connectors: Build your own if needed.
• ELT Support: Simple to complex data movement.

7. Appwrite

A self-hosted backend-as-a-service. Build scalable apps with ease.

Key Features:

• Database and Storage: Secure data and file management.
• Authentication and Authorization: Robust user access control.
• Serverless Functions: Run backend code without servers.
• Realtime Capabilities: Build interactive features.

8. Ory Kratos

Open-source identity management. Security and developer focus.

Key Features:

• Multi-Factor Authentication (MFA): Enhanced security for users.
• Passwordless Authentication: Modern login options.
• Identity Federation: Integrate with other identity systems.
• Flexible User Schemas: Customize user profiles.

9. Coolify

A self-hosted PaaS alternative. Simple deployment of web apps.

Key Features:

• Simplified Deployment: Deploy with a few clicks.
• Automatic SSL Certificates: Free SSL via Let’s Encrypt.
• Resource Management: Monitor and scale resources.
• Support for Multiple Application Types: Versatile deployment.

10. n8n

Free, open-source workflow automation. Connect apps visually.

Key Features:

• Node-Based Visual Editor: Design workflows easily.
• Extensive Integration Library: Connect to many services.
• Customizable Nodes: Integrate with anything.
• Self-Hostable: Full data control.

11. LLMWare

Build LLM-powered applications. Open-source tools and frameworks.

Key Features:

• Prompt Management: Organize and test prompts.
• Data Ingestion and Indexing: Prepare data for LLMs.
• Retrieval Augmented Generation (RAG): Ground LLM responses.
• Deployment Options: Flexible deployment choices.

12. Trieve

Open-source vector database. Efficient semantic search.

Key Features:

• Efficient Vector Storage and Retrieval: Fast similarity search.
• Multiple Distance Metrics: Optimize search accuracy.
• Metadata Filtering: Refine search results.
• Scalability: Handles large datasets.

13. Affine

Open-source knowledge base and project tool. Notion and Jira combined.

Key Features:

• Block-Based Editor: Flexible content creation.
• Database Functionality: Structured information management.
• Project Management Features: Task and progress tracking.
• Interlinking and Backlinks: Connect your knowledge.

14. Hanko

Open-source passwordless authentication. Secure and user-friendly.

Key Features:

• Passwordless Authentication: Secure logins without passwords.
• WebAuthn Support: Industry-standard security.
• User Management: Easy account and key management.
• Developer-Friendly APIs: Simple integration.

15. Taubyte

Open-source edge computing platform. Run apps closer to users.

Key Features:

• Decentralized Deployment: Deploy across edge nodes.
• Serverless Functions at the Edge: Low-latency execution.
• Resource Optimization: Efficient resource use.
• Scalability and Resilience: Robust and scalable apps.

16. Plausible

Lightweight, privacy-friendly web analytics. An alternative to Google.

Key Features:

• Simple and Clean Interface: Easy-to-understand metrics.
• Privacy-Focused: No cookies, no personal tracking.
• Lightweight and Fast: Minimal impact on site speed.
• Self-Hostable: Own your data.

17. Flipt

Open-source feature flags and experimentation. Safe feature rollouts.

Key Features:

• Feature Flag Management: Control feature visibility.
• A/B Testing: Run controlled experiments.
• Gradual Rollouts: Release features slowly.
• User Targeting: Target specific user groups.

18. PostHog

Open-source product analytics. Understand user behavior.

Key Features:

• Event Tracking: Capture user interactions.
• Session Recording: See how users behave.
• Feature Flags: Integrated feature control.
• A/B Testing: Experiment and analyze.

19. Logto

Open-source authentication and authorization. Modern app security.

Key Features:

• Flexible Authentication Methods: Various login options.
• Fine-Grained Authorization: Granular access control.
• User Management: Easy user and permission management.
• Developer-Friendly SDKs: Simple integration.

20. NocoDB

Open-source no-code platform. Turn databases into spreadsheets.

Key Features:

• Spreadsheet-like Interface: Familiar data interaction.
• API Generation: Automatic REST and GraphQL APIs.
• Form Builders: Create custom data entry forms.
• Collaboration Features: Teamwork on data and apps.

The open-source world offers great SaaS alternatives. You can cut costs and gain control. Explore these tools and free yourself from high SaaS bills. Take charge of your software stack.

Wow this is going to be huge.

I’ve been testing Claude’s new ability to generate complex interactive diagrams on the fly and I’ve been absolutely blown away by what I’ve seen. The future is here without a doubt.

Claude can now visualize literally any concept you can think of in this world.

I asked it to visualize a network request and the results were completely insane — it illustrated everything so easily and effortlessly.

All from a single, dead simple prompt — not even up to 10 words:

And the keyword here is interactive — these are not just passive diagrams for you to observe — many of them will actually let you tweak settings and see how various parameters work in your visualization for deeper understanding.

Like this side-by-side sorting algorithm visualization I asked it to do — I was able to adjust options like the array size and the sorting speed.

They’re like mini-apps generated on the fly — we’re definitely heading in this direction right now.

You can even click on them for more details — it will automatically send a new prompt — which will generate a new diagram 👇

You could break down the most intricate systems into their deepest, most fundamental foundational concepts:

In this article I asked it to generate 21 different diagrams — in various aspects of Computer Science and software dev, from system design to AI to networking — and it was unbelievable. It just kept delivering.

Algorithms & data structures

1. Graph traversal (BFS vs DFS)

I asked it to show visually show me the different between breadth-first and

2. Sorting algorithm race

3. Memory layout of data structures

4. Hash table collision handling

System design & architecture

5. Microservices architecture map

6. Event-driven architecture flow

7. Distributed system with CAP tradeoffs

8. Kubernetes cluster anatomy

Artificial intelligence

9. Transformer architecture (“attention is all you need…”)

10. Neural network

Backend & infrastructure

11. Request lifecycle (end-to-end)

12. Database query execution plan

13. Caching strategy layers

14. CI / CD pipeline flow

Network & protocols

15. TCP vs UDP communication flow

16. DNS resolution journey

17. WebSocket vs HTTP lifecycle

Security & cryptography

18. Public-key encryption flow

19. OAuth 2.0 authentication flow

20. Zero trust security architecture

Concurrency & runtime behavior

21. Multithreading & event loop comparison

Small features can change workflows massively — and the new /btw is definitely one of such.

❌ Before:

This is what many of us are used to right now — every question you ask an AI coding assistant becomes part of the same growing conversation thread:

No /btw feature used to ask questions here 👇:

Which unfortunately leads to questions, clarifications, and quick reminders slowly cluttering the context — making sessions more expensive than ever.

✅ Now:

Now Claude Code’s new /btw command is here to change all that — by creating a lightweight lane for disposable, context-aware questions.

Making changes just before using /btw:

Asking questions on our changes with /btw:

When we press Enter, the btw message disappears and we’re back to our normal conversation:

It’s a simple but powerful feature that makes those long coding sessions cleaner and much more efficient.

What /btw actually is

/btw is a lightweight side-question feature inside Claude Code. It can see the current session context, meaning it understands the code, decisions, and task state already in play.

But unlike the main thread, it’s intentionally constrained.

1. Context-aware

/btw understands the active session.

That means you can ask questions tied to the current work, such as:

Making changes to our codebase:

• “What does this regex do?”
• “What is this helper function responsible for again?”
• “Why did we choose this configuration pattern earlier?”

Using /btw to ask context-aware questions:

You’re not asking a model with zero memory. You’re asking from inside the active coding session, where the relevant context already exists.

That’s what makes /btw more useful than opening a separate AI chat.

2. Disposable history

The core idea behind /btw is that the interaction is temporary.

Your question and its answer do not become part of the main conversation history.

Making changes to our codebase:

Why this matters:

Many developer questions are momentary:

• reminders
• clarifications
• quick explanations

Using /btw to ask questions:

Claude Code doesn’t remember the previous /btw message:

You need the answer right now, but the agent doesn’t need to keep re-reading that exchange for the rest of the session.

Think of /btw as:

• a sticky note, not a commit message
• a side whisper, not a meeting transcript

3. Read-only

Another important constraint:

/btw cannot perform actions.

Making changes to our codebase:

It cannot:

• edit files
• run bash commands
• use MCP tools
• inspect new files

It can only talk.

Trying to make changes with /btw — it doesn’t work — it only shows a message containing the code at best:

This means /btw isn’t meant for implementation work. Instead, it’s meant for:

• explanations
• reminders
• quick clarifications
• contextual understanding

You use /btw to stay oriented while real work continues elsewhere.

4. Single-turn only

/btw also enforces a strict interaction model:

One question. One answer.

There’s no extended back-and-forth and no mini-thread forming inside the /btw window.

That constraint prevents it from turning into a secondary conversation.

It stays exactly what it’s meant to be: a quick aside.

How it helps us as developers

The real value of /btw isn’t the command itself.

It’s how it improves workflow during long, context-heavy coding sessions.

1. Massive token savings

Claude Code stays effective by remaining aware of the full conversation history.

But that also means every new turn in the main thread carries the cost of everything that came before it.

As sessions grow, small interruptions become expensive.

For example:

• A 40-message thread means Claude rereads a large context every turn.
• Adding clarification questions increases that cost quickly.

This is where /btw helps.

Instead of putting these into the main thread:

• “What does this regex do?”
• “What was this utility function for again?”
• “What does this flag change?”

You route them through /btw.

The result:

• fewer tokens in the main thread
• less history to reread
• significantly cheaper long sessions

Over time, this can cut total session costs dramatically.

2. It prevents context rot

Long AI conversations naturally degrade.

As threads grow, the context becomes noisy:

• temporary explanations
• side questions
• dead-end ideas
• minor clarifications

Eventually the model starts losing track of what matters.

Developers often see this as:

• missed constraints
• forgotten earlier decisions
• weaker reasoning

This is essentially context rot.

/btw helps prevent this by enforcing separation:

Main thread

• implementation
• architecture decisions
• debugging
• planning
• file edits

/btw

• explanations
• quick reminders
• clarifications
• trivia

Keeping those categories separate helps the main thread remain clean and focused, which maintains higher reasoning quality for longer.

3. Seamless multitasking

This is the most underrated benefit.

During long-running Claude tasks—like refactors or multi-file updates—you’ll often have small questions.

For example:

• “What syntax does this function expect again?”
• “What does that pattern mean?”
• “Did we say this utility handles validation?”

Without /btw, you have two options:

1. Interrupt the main thread and risk derailing the workflow.
2. Hold the question in your head and slow yourself down.

/btw gives you a third option.

You can quickly ask the question without altering the agent’s working context.

This makes Claude feel less like a fragile chat log and more like a collaborator that can handle quick side questions while staying focused on the main task.

The real takeaway

The best way to think about /btw is this:

It’s a pressure-release valve for long Claude Code sessions.

It allows developers to:

• ask context-aware questions
• avoid polluting the main thread
• reduce token usage
• preserve reasoning quality
• multitask more smoothly

For developer workflows, that’s not a flashy feature.

It’s just good interface design.

Most AI tools treat every interaction as permanent. /btw recognizes that real development doesn’t work that way.

Some questions matter long-term — others are just momentary.

Treating those differently is exactly why /btw matters.

Awesome news:

The new 1 million token context window in Claude Sonnet 4.6 and Opus 4.6 is now generally available to everyone.

And this number isn’t just a bigger number for Anthropic to brag about — it fundamentally changes how AI can assist us with real software development.

Before:

Coding with AI often meant constantly shrinking problems to fit the model’s memory with context compaction and other hacky techniques.

But now:

Entire codebases, multi-hour long debugging sessions, and intricately connected system context can all stay in view at once.

And not all 1M token context windows are equal you know… other models like GPT and Gemini do have this too — but Claude’s 1M context outshines them — due to its superior long context retrieval — as seen in major benchmarks.

With 1M context now the default, no extra pricing for long inputs, and major improvements in long-context retrieval, developers can finally use AI on the most enormous projects without the any of the usual limitations and accuracy losses.

Let’s look at 5 key ways these changes translate into meaningful upgrades for our everyday coding workflows.

1. Superior whole-codebase understanding leads to smarter fixes and refactors

Context lost — one of the most common frustrations with AI coding tools.

Models often understand a single file well but struggle with how that file fits into the broader system — especially when they can’t fit in all the related files into the context.

With 1M tokens available, Claude can hold far more project information at once, including:

• Multiple services or modules
• API contracts and schemas
• Tests and test expectations
• Documentation and architecture notes
• Logs, stack traces, and debugging context

Instead of constantly reintroducing information, developers can load much more of the codebase into context from the start.

This leads to improvements in tasks like:

• Debugging issues that span multiple files
• Performing architecture-aware refactors
• Maintaining consistency with existing patterns
• Understanding how changes affect dependent modules

Claude becomes much more like a collaborator that understands the full structure of the project — no matter how massive it gets.

2. Pinpointing critical details hidden in massive codebases

Large context windows only help if the model can actually find the right information inside them.

Anthropic measures this with long-context retrieval tests called needle-in-a-haystack benchmarks, which test whether a model can locate a specific fact buried inside extremely large inputs.

Recent results show us just how much this capability has improved:

• Opus 4.6 scored 76% on the 1M-token MRCR benchmark
• Sonnet 4.5 scored around 18% on similar tests

This huge jump shows how Anthropic is improving context navigation, not just context size.

Why this matters for developers:

• A model can find the single relevant interface or function hidden in a huge repo
• It can trace dependency chains across many files
• It can identify the exact assumption that caused a test failure

Without strong retrieval, large contexts become noise. With it, they become a powerful reasoning workspace.

3. Maintaining context across long debugging and development sessions

Real coding work rarely happens in a single prompt. Instead, it unfolds through long investigative sessions:

1. Inspect code
2. Read logs
3. Test hypotheses
4. Make changes
5. Verify results
6. Iterate

Smaller context windows force models to forget earlier discoveries during long sessions.

With 1M tokens, much more of the session history can remain active.

This significantly reduces reliance on context compaction, which summarizes older conversation history when context limits are reached.

The result:

• Fewer lost insights during debugging
• Less repetition explaining earlier findings
• More coherent multi-step reasoning

For long debugging sessions or large migrations, this continuity becomes extremely valuable.

4. Faster iteration by reusing full project context efficiently

Large context windows pair naturally with prompt caching, which allows repeated prompts to reuse previously processed tokens.

This is particularly powerful for coding workflows where a large base context stays mostly the same, such as:

• A full repository snapshot
• API schemas and documentation
• Coding guidelines
• Build instructions and tooling notes

Instead of reprocessing that entire block every time, caching allows follow-up prompts to reuse the existing context.

Benefits include:

• Faster responses for repeated queries
• Lower token costs across long sessions
• More efficient agent workflows

Combined with a 1M window, caching makes “load the codebase once and iterate” a practical development workflow.

5. Making large-scale AI coding practical for everyday development

Perhaps the most developer-friendly change is pricing.

Previously, large context windows often came with extra costs or special tiers. Anthropic removed that friction.

Key changes:

• No extra charge for long context
• 1M tokens available by default
• Standard pricing applies across the full window

For Sonnet 4.6:

• $3 per million input tokens
• $15 per million output tokens

This means a 900K-token prompt costs the same per token as a 9K prompt.

Combined with prompt caching, this dramatically lowers the cost of long-context coding workflows.

Developers can now:

• Load much larger codebases
• Keep more session history intact
• Run deeper investigations

—without worrying that large context usage will suddenly spike costs.

The real upgrade

Claude Sonnet’s 1M token context is not just a bigger memory limit.

It’s a combination of improvements that work together:

• 1M context now available by default
• No premium pricing for long context
• Major improvements in long-context retrieval
• Better synergy with prompt caching
• Reduced reliance on context compaction

Together, these changes allow Claude to work with entire systems instead of isolated snippets.

And for developers, that means fewer broken assumptions, better debugging, and more reliable help on the kinds of complex problems that actually dominate real-world coding.