AICL using @tryAlanAI
Real crazy use of llm as a judge with real life implementation
Useful for teams wanting to collaborate at development level while keeping all the failures in mind so that the failure rates can be reduced as much as possible
github.com/tusharrworkk-lab/…
Today we're launching @tryAlanAI so developers can stop babysitting their coding agents.
We kept running into the same problem ourselves.
Even as agents got better at writing code, we were becoming glorified button-pushers for our own AI: starting every run, feeding them context, unblocking failures, and checking their work.
We built Alan to change that.
Features:-
→ Canonical trace ingestion & failure fingerprinting
→ Recurrence detection & recovery strategy synthesis
→ Evaluation-driven strategy validation using LLM-as-a-Judge
→ MCP-powered context retrieval
→ Privacy-preserving cross-agent learning
Crazy dev by @tryAlanAI
AICL using @tryAlanAI
Real crazy use of llm as a judge with real life implementation
Useful for teams wanting to collaborate at development level while keeping all the failures in mind so that the failure rates can be reduced as much as possible
github.com/tusharrworkk-l…
Today we're launching @tryAlanAI so developers can stop babysitting their coding agents.
We kept running into the same problem ourselves.
Even as agents got better at writing code, we were becoming glorified button-pushers for our own AI: starting every run, feeding them context, unblocking failures, and checking their work.
We built Alan to change that.
github.com/tusharrworkk-lab/…
This is the link sorry for the issue with the link
Features:-
→ Canonical trace ingestion & failure fingerprinting
→ Recurrence detection & recovery strategy synthesis
→ Evaluation-driven strategy validation using LLM-as-a-Judge
→ MCP-powered context retrieval
→ Privacy-preserving cross-agent learning
Crazy dev by @tryAlanAI
AICL
Real crazy use of llm as a judge with real life implementation
Useful for teams wanting to collaborate at development level while keeping all the failures in mind so that the failure rates can be reduced as much as possible
github.com/tusharrworkk-lab/…
@prasad_pilla @tryAlanAI
Building AICL
→ Canonical trace ingestion & failure fingerprinting
→ Recurrence detection & recovery strategy synthesis
→ Evaluation-driven strategy validation
→ MCP-powered context retrieval
→ Privacy-preserving cross-agent cross tenant learning
@prasad_pilla @tryAlanAI
I think it is high time they should start releasing a bit more information about the architecture of the model in depth as well so that we can see what really does happen inside the model in a way
Tushar retweeted
Shocking news! Demis Hassabis is stepping down as CEO of Google DeepMind, and Jeff Dean is leaving Google to start his own company. Sir Demis will be the new chief scientist.
Kimi K3 introduces Delta Attention
Instead of storing every key-value pair, it maintains a fixed-size memory matrix and writes only the delta (difference).
Less memory. Linear scaling. Million-token context becomes far more practical.