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@akshay_pachaar

PixelRAG: visual retrieval system that screenshots pages instead of scraping

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@akshay_pachaar

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20 June 2026 14h59

Web scraping will never be the same.

(100% open-source visual search at scale)

PixelRAG is a retrieval system that skips HTML parsing completely.

Instead of scraping a page into text and embedding chunks, it screenshots the page and retrieves the image. A vision-language model… https://t.co/tYKRgKDvSR pic.twitter.com/3dxYra0tZZ
— Akshay 🚀 (@akshay_pachaar) 20 juin 2026

Web scraping will never be the same. (100% open-source visual search at scale) PixelRAG is a retrieval system that skips HTML parsing completely. Instead of scraping a page into text and embedding chunks, it screenshots the page and retrieves the image. A vision-language model

→ View original post on X — @akshay_pachaar

20 June 2026
Karpathy’s RL prediction: reward functions unreliable, need knowledge-guided review

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@akshay_pachaar

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19 June 2026 15h55

Karpathy's prediction about RL is coming true now!

He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a… https://t.co/0REApdfBUG pic.twitter.com/uAfW9yvn3m
— Akshay 🚀 (@akshay_pachaar) 19 juin 2026

Karpathy's prediction about RL is coming true now! He called reward functions unreliable and argued that a single reward number is too low-dimensional to teach an agent what "good" means for complex tasks. To solve this, Agents need a knowledge-guided review as a

→ View original post on X — @akshay_pachaar

19 June 2026
Inverse scaling: weak model gains from harness patching runtime gaps

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@akshay_pachaar

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17 June 2026 12h46

Exactly the inverse-scaling point. The weak model gained most because the harness patched gaps it couldn't fix on its own. Cheaper to evolve the runtime than to scale the model, and you keep the smaller footprint at inference.

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17 June 2026
HarnessX self-compiles; Anthropic and Manus remove complexity iteratively

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@akshay_pachaar

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16 June 2026 16h13

HarnessX: a harness that compiles itself. every harness improvement so far has come from a human editing code by hand. Anthropic strips planning steps out of Claude Code when a stronger model ships. Manus rebuilt its agent five times in six months, removing complexity each

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16 June 2026
Train LLM from scratch and AI Engineering book resources

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@akshay_pachaar

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15 June 2026 18h01

Train LLM from scratch: https://
github.com/FareedKhan-dev
/train-llm-from-scratch
…
—
AI Engineering book: https://
dailydoseofds.github.io/ai-engg-book/

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15 June 2026
Build a GPT-style transformer from scratch without high-level libraries

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@akshay_pachaar

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15 June 2026 18h01

Train your own LLM from scratch. This repo builds a GPT-style transformer from the ground up, without using any high-level libraries. You see exactly how attention, multi-head attention, the feed-forward block, embeddings, residuals, and layer norm fit together. And it doesn't

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15 June 2026
Pourquoi 95% des projets IA échouent en entreprise

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@akshay_pachaar

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15 June 2026 9h32

95% of enterprise AI projects fail to deliver. but rarely because the model is bad. they fail because teams can’t answer the harder questions: – how do we govern this?
– how do we prove ROI?
– how do we deploy it safely in regulated environments?
– how do we move beyond

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15 June 2026
Google DeepMind paper maps the path from AGI to ASI

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@akshay_pachaar

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14 June 2026 14h30

From AGI → ASI Google just mapped what comes after human-level AI. there's a brilliant new paper from Google DeepMind called "From AGI to ASI," and it skips the fight everyone else is having. almost every AI debate today is about reaching human-level intelligence. this one

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14 June 2026
Top Claude Code CLI integrations for GitHub superpowers

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@akshay_pachaar

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13 June 2026 14h53

The top Claude Code CLI integrations to give you superpowers:

1. GitHub

The repo stops being a folder of files and becomes something the agent actually runs.

It reads and writes issues, PRs, Actions, and releases, so it works the codebase the way an engineer does, not by… https://t.co/vkws3sfWNu pic.twitter.com/s1pES1yZIq
— Akshay 🚀 (@akshay_pachaar) 13 juin 2026

The top Claude Code CLI integrations to give you superpowers: 1. GitHub The repo stops being a folder of files and becomes something the agent actually runs. It reads and writes issues, PRs, Actions, and releases, so it works the codebase the way an engineer does, not by

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13 June 2026
Claude Code: no visibility into production issues

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@akshay_pachaar

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13 June 2026 10h06

You'll regret using Claude Code without this.

Claude Code stops at the boundary of your terminal.

It has no visibility into Datadog traces, Jira tickets, Slack decision history, or cloud infra state.

So when something breaks in production, Claude Code isn't there. When the… pic.twitter.com/igWgs8AhQl
— Akshay 🚀 (@akshay_pachaar) 13 juin 2026

You'll regret using Claude Code without this. Claude Code stops at the boundary of your terminal. It has no visibility into Datadog traces, Jira tickets, Slack decision history, or cloud infra state. So when something breaks in production, Claude Code isn't there. When the

→ View original post on X — @akshay_pachaar

13 June 2026

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