initlayers

A week ago, if you told me I'd be working with people from OpenClaw and Hugging Face, I'd have laughed. Funny how much can change in a week.

Bro saw the labour laws and took it as a list of suggestions

A few minutes later

“Join a startup bro, you’ll grow so much”

Awesome!!

I have come across this paper by Apple. Their problem statement was whether RL can train agents to perform Long Horizon multi step tasks in interactive environment instead of teaching them from static examples. They have used AppWorld as a simulated environment that contains multiple apps and APIs and trained Qwen2.5-32B-Instruct model under 24 scenarios (72 tasks). The model was not given any instructions on how to solve each task and was left to interact with the environment and learn from the environment rewards based on task outcomes. In short they were using an RL method LOOP that doesn't use a value network and keeps only one copy of the underlying LLM in memory. They trained a 32B agent which reached 71.3% task completion on the normal AppWorld test setting. Also OpenAI o1 scored 61.9% on the same setting, so the 32B agent beat o1 by about 9 percentage points. The paper reports this as a 15% relative improvement. So what's actually interesting in this was the agent's behaviour? It became more likely to read API documentation, made fewer unnecessary assumptions, reduced hallucination, and got better at recovering from setbacks and errors. If I add a different perspective, the agent learned better interaction strategies instead of better answers. The important finding is that they trained a relatively small agent in an environment, and RL changed the way the agent went about solving problems. Paper: Reinforcement Learning for Long-Horizon Interactive LLM Agents by Kevin Chen et al.

Bro, what the fk is this calendar? 💀

I think they have fixed this issue in the recent update.

My GF's first day at work 😊 She lasted approximately 9 hours before broadcasting the chaos at her office.

Today I solved Valid Sudoku from NeetCode 250. I started with a complicated list slicing approach, then realized I needed to track rows, columns and boxes with hash sets. I initially created 27 sets manually cuz I'm afraid of defaultdict(set). But I finally learnt it.

Time to find out how updated my professors are with the current industry. Mock interview season begins. Let's see what questions they come up with.

4 August 2026 Today's NeetCode 250 problem was Range Sum Query 2D - Immutable. I started with the naive approach, but it quickly became obvious why it doesn't scale when there are lots of queries. Then I learned the 2D Prefix Sum technique, and it completely changed how I looked at the problem. Precompute once, answer every query in constant time. On the backend side, I started learning Pytest. I practiced on a few sample files, then configured a separate test database for my FastAPI and began writing tests for my API functions.