You spend hours building something incredible with AI.
Everything’s going well.
Then out of nowhere, your AI forgets what it was doing.
Variables vanish.
The logic breaks.
You spend the next hour reminding it what you already told it.
That’s the AI coding context memory problem.
The single biggest flaw in AI development today.
And Google just dropped the fix.
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The Problem: AI Loses Context After 20 Messages
Every developer has hit this wall.
You start coding with Claude, GPT, or Gemini.
At first, it’s magic — clean code, perfect suggestions.
Then message 20 hits.
Suddenly, the model forgets what tech stack you’re using.
It references variables that don’t exist.
It suggests libraries you already removed.
Why?
Because most AI models don’t have true AI coding context memory.
They treat every message like a new conversation.
No long-term understanding of your project.
No persistent plan or file awareness.
Just a string of chat bubbles pretending to remember.
That’s why AI keeps losing track of your work.
Introducing Gemini Conductor
Google decided to fix this with a tool called Gemini Conductor.
Released December 17, 2025, it’s an upgrade for Gemini CLI.
But this isn’t just another chat interface — it’s a revolution.
Gemini Conductor introduces context-driven development.
Instead of temporary chat memory, it builds AI coding context memory right into your repo.
Here’s how it works.
Context-Driven Development Explained
When you run “conductor setup,” Gemini scans your codebase.
It studies your structure, your naming patterns, your files.
Then it creates persistent markdown files called “specs.”
Those specs store everything the AI learns about your project.
Your goals, architecture, and decisions are now part of your codebase.
That means when you pause and come back later, nothing’s lost.
The AI reads your spec, reloads your AI coding context memory, and continues exactly where it left off.
No repetition.
No forgotten details.
Just seamless continuation.
Why This Changes Everything
Developers waste hours re-explaining context every session.
With Gemini Conductor, you don’t start from scratch.
You start from memory.
Imagine building an app, taking a week off, and resuming without losing a single detail.
That’s what context-driven development enables.
You can version control the AI’s understanding, share it with teammates, and update it as your project evolves.
It’s the first time AI truly “remembers” your codebase.
The Power of GLM 4.7
Now imagine combining that structure with raw coding power.
That’s where GLM 4.7 comes in.
Built by Z.AI and released on December 22, 2025, it’s a coding-first model — purpose-built for developers.
Where GPT and Claude aim for general intelligence, GLM focuses entirely on writing and debugging code.
It’s fast, precise, and most importantly, consistent.
The perfect complement to Gemini Conductor’s AI coding context memory.
Benchmark-Proven Performance
On SWE-Bench, GLM 4.7 scores 73.8%.
On Terminal Bench 2.0, it hits 41%.
Those are huge jumps over previous models.
But the real reason it’s revolutionary is how it thinks.
GLM 4.7 uses interleaved reasoning — it pauses before every code output to plan.
No more instant hallucinations or broken logic.
It reasons through each step before writing.
That makes the code cleaner, safer, and more maintainable.
Preserved Thinking = Persistent Accuracy
Another breakthrough is what Z.AI calls preserved thinking.
GLM 4.7 retains its logic across multiple turns.
That means it maintains internal consistency in long coding sessions.
In other words, it finally has real AI coding context memory built in.
Your AI won’t contradict itself after 10 turns.
It builds a mental map of your project and sticks to it.
Turn-Level Control
GLM 4.7 even lets you control how much reasoning the AI uses.
Simple task? Lower the reasoning budget for speed.
Complex problem? Increase it for accuracy.
You decide the balance.
And since it runs at 1/7th the cost of Claude, you can build nonstop without worrying about API bills.
That’s a game-changer for teams running agents 24/7.
Combining Gemini Conductor and GLM 4.7
Now here’s where it gets insane.
Combine Gemini Conductor’s memory system with GLM 4.7’s reasoning engine.
You get a persistent AI that understands your project and executes it perfectly.
The specs Conductor creates become a shared brain for your entire coding workflow.
GLM reads those specs before coding, uses them to stay aligned, and writes coherent code that matches your structure.
That’s full AI coding context memory in action.
Real Use Cases
If you’re running a SaaS business, this combo accelerates feature delivery.
Teams can collaborate asynchronously without losing context.
Freelancers can manage multiple projects without re-teaching the AI.
Educators can use it to document learning steps for students.
It’s practical, scalable, and already reshaping how developers build software.
From Forgetful Chatbots to Reliable Builders
Let’s be honest — traditional AI coding tools behave like forgetful interns.
They start strong, then lose track halfway through.
But now, with AI coding context memory, you’re training a reliable assistant that builds like a senior engineer.
It remembers dependencies, logic flows, and the reasoning behind your choices.
That’s the difference between chaos and consistent progress.
Smarter UI, Better Design
Z.AI also introduced something called vibe coding with GLM 4.7.
It boosts layout compatibility from 52% to 91%.
That means cleaner UIs, better alignment, and professional-looking designs — all generated by AI.
You’re not just getting functional code.
You’re getting visually consistent applications straight out of the model.
Learn and Build With the AI Success Lab
If you want the exact workflows for AI coding context memory, join Julian Goldie’s FREE AI Success Lab Community:
https://aisuccesslabjuliangoldie.com/
Inside, you’ll see how developers use Gemini Conductor and GLM 4.7 to automate builds, document codebases, and debug faster.
You’ll get templates, SOPs, and workflows you can copy right now.
It’s where 38,000+ members are building smarter, not harder.
Practical Tips
Always review Conductor’s plans before you approve them.
Catch logic issues early instead of debugging them later.
Commit your spec files to version control so teammates can stay synced.
Use the revert command to undo changes safely when something breaks.
These habits make AI coding context memory your competitive edge.
FAQ
What is AI coding context memory?
It’s the ability for AI models to remember your entire project context across multiple interactions.
How does Gemini Conductor use it?
It creates persistent markdown specs that record project knowledge directly inside your codebase.
What makes GLM 4.7 special?
It’s a coding-first model with reasoning and memory features that drastically reduce errors.
Can I use them together?
Yes. Conductor handles structure, GLM handles execution — the perfect pairing for long-term builds.
Is this free or paid?
Conductor is free in preview. GLM 4.7 is open source and available right now.
Final Thoughts
AI development is evolving from chat to memory.
From short-term sessions to long-term collaboration.
AI coding context memory is the breakthrough that finally makes AI practical for real-world builds.
With Gemini Conductor and GLM 4.7, your AI doesn’t forget.
It learns, adapts, and builds alongside you.
That’s the future of coding — and it starts now.
