RIP fine-tuning ☠️

Forget fine-tuning. This Stanford + SambaNova paper just killed it.👀

LLMs self-improve… without fine-tuning. How?

What if your LLM could fine-tune itself… without ever touching weights?

It’s called 'Agentic Context Engineering (ACE)' and it proves you can make models smarter without touching a single weight.

Instead of retraining, ACE evolves the context itself.

The model writes, reflects, and edits its own prompt over and over until it becomes a self-improving system.

Think of it like the model keeping a growing notebook of what works.
Each failure becomes a strategy. Each success becomes a rule.

The results are absurd:

+10.6% better than GPT-4–powered agents on AppWorld.
+8.6% on finance reasoning.
86.9% lower cost and latency.
No labels. Just feedback.

Everyone’s been obsessed with “short, clean” prompts.

ACE flips that. It builds long, detailed evolving playbooks that never forget. And it works because LLMs don’t want simplicity, they want *context density.

If this scales, the next generation of AI won’t be fine-tuned.
It’ll be self-tuned.

The next AI era isn't fine-tuned… It's self-tuned

(0/1) How ACE works :

Agentic Context Engineering (ACE) enhances LLMs by dynamically evolving prompts through three roles:

into 3 roles:

Generator - runs the task
Reflector - critiques what went right or wrong
Curator - updates the context with only what matters

Each loop adds delta updates, small context changes that never overwrite old knowledge.

It’s literally the first agent framework that grows its own prompt.

(1/2)

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A Thread 🧵👇

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Insecure CORS Configuration" vulnerabilities. 🛡️⚔️

[A thread 🧵]

#infosecurity #CyberSec #bugbountytips #cybersecurity [2/n]
What is Insecure CORS issue?

An insecure CORS configuration allows any website to trigger requests with user credentials to the target application and read the responses thus enabling attackers to perform privileged actions or to retrieve potential sensitive information

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