You're in a Research Scientist interview at Google. Interviewer: We have a base LLM that's terrible at maths. How would you turn it into a maths & reasoning powerhouse? You: I'll get some problems labeled and fine-tune the model. Interview over. Here's what you missed:
@akshay_pachaar
-
How to Write Skills That Never Fail for AI Agents
By
–
How to write Skills that never fail.
— Akshay 🚀 (@akshay_pachaar) 1 mai 2026
Writing good skills for agents is one of the highest-leverage skills you can develop today.
But many Claude skills fail silently. They never trigger, they trigger on the wrong prompts, or they skip the steps they were written to enforce.… pic.twitter.com/IeEgQCPuWWHow to write Skills that never fail. Writing good skills for agents is one of the highest-leverage skills you can develop today. But many Claude skills fail silently. They never trigger, they trigger on the wrong prompts, or they skip the steps they were written to enforce.
-
Agent Latency Optimization: Router and Manager Bottlenecks
By
–
Agreed on both. Latency depends mostly on the agent blocks, not the connections themselves (those are basically free). With a decent local model the bottleneck is inference time per agent call, so the router and manager agents dominate. Most of the 29 edges are just data
-
Error Handling and Retry Mechanisms in AI Agent Blocks
By
–
Good question. Sim has retries and error paths built into each block, so you can set retry counts and route failures to a fallback branch. For the agent blocks, I also keep prompts strict on output format so downstream parsing doesn't break. Haven't needed heavy fallback
-
Agent Environment Architecture: Memory Routing Integration
By
–
Thanks! That framing is exactly how I started seeing it too. Once memory, routing, and multi-channel sit in one place, the workflow stops feeling like a script and starts feeling like an environment the agents actually live in.
-
AI-Generated Workflow Structures and Intelligent Branching Logic
By
–
Yeah, Mothership generated the structure from a prompt. For branching, it picks the right block (router for classification, condition for if/else, parallel for fan-out) and wires them up. I usually tweak the routes and prompts after, but the skeleton holds up surprisingly well.
-
Dual Memory Architecture: Short-term Manager and Long-term Supabase Retrieval
By
–
Hey, there are two memory layers running in parallel. Short-term lives on the manager agent (keyed to the user). Long-term embeds each turn into Supabase and pulls relevant past context back before the model sees the new message. State lives outside the model, so swapping
-
Rebuilding OpenClaw AI Agent with Open-Source Stack
By
–
I rebuilt most of OpenClaw's core in a single workflow:
— Akshay 🚀 (@akshay_pachaar) 29 avril 2026
– 25 blocks
– 29 connections
– Short + long-term memory
– Multi-channel (Telegram + Slack)
Didn't build it manually. Stack is fully open-source. Self-host, run local models, own it end-to-end.
Full walkthrough:
Chapters:… https://t.co/ESiNHqwD4h pic.twitter.com/hawcm2diJuI rebuilt most of OpenClaw's core in a single workflow: – 25 blocks
– 29 connections
– Short + long-term memory
– Multi-channel (Telegram + Slack) Didn't build it manually. Stack is fully open-source. Self-host, run local models, own it end-to-end. Full walkthrough: Chapters: -
Vector Databases Limitations in Multi-Hop Reasoning Tasks
By
–
Vector DBs can't reason.
— Akshay 🚀 (@akshay_pachaar) 29 avril 2026
Top-k similarity ranks chunks one at a time against a query. That's fine for single-hop fact lookups, and it breaks the moment a question needs information stitched across multiple chunks.
That's what the FalkorDB GraphRAG-Bench results expose. The gap… https://t.co/hCv4cwP7Xk pic.twitter.com/utrYDQjFwZVector DBs can't reason. Top-k similarity ranks chunks one at a time against a query. That's fine for single-hop fact lookups, and it breaks the moment a question needs information stitched across multiple chunks. That's what the FalkorDB GraphRAG-Bench results expose. The gap
-
Live AI Tool Launch with Research Paper Available
By
–
Before you dive into the paper, you can try it live here: http://
plurai.ai/launch Paper: http://
plurai.ai/papers