Imanuel Age

AGEiDeas. Dreams do Come True 🤓

[Mike Piccolo @mfpiccolo]

https://t.co/RtApn0Z2Gw

[Tanishq Mathew Abraham, Ph.D. @iScienceLuvr]

Autodata: An agentic data scientist to create high quality synthetic data

"We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data."

Data creation stage + data analysis stage+meta-optimization

[Imanuel Age @ImanuelAge]

@Manu_Sisti PDF

[Ahmad @TheAhmadOsman]

The Ultimate Step-By-Step LLM Engineering Projects Roadmap (2026 Edition)

- Build a tokenizer
- Learn embeddings
- Implement RoPE / ALiBi
- Hand-wire attention
- Build MHA
- Build a Transformer block
- Train a mini-former
- Compare objectives
- Build sampling
- Speculative decoding
- KV cache
- MQA / GQA / MLA
- Long context
- FlashAttention
- Hardware budgets
- Toy MoE
- Sparse model trade-offs
- State-space / linear attention
- Diffusion language models
- Data pipelines
- Synthetic data
- Scaling laws
- SFT / DPO / RLHF / GRPO
- Quantization
- Serving stacks
- Eval harnesses
- RAG
- Tool use / agents
- Vision-language adapters
- Interpretability
- Red-team suite
- Full capstone model system

One request: Choose an Opensource AI lab when you make it

Opensource is where humanity gets to keep the tools

DM me when you've made it ;)

[Imanuel Age @ImanuelAge]

@NadzuAI Claude

[Imanuel Age @ImanuelAge]

@AndrewBolis YT

[Mari @Tech_girlll]

list of 10 active AI researchers on X:

1. @karpathy — Andrej Karpathy
2. @rasbt — Sebastian Raschka
3. @AndrewYNg — Andrew Ng
4. @drfeifei — Fei-Fei Li
5. @demishassabis — Demis Hassabis
6. @GaryMarcus — Gary Marcus
7. @ykilcher — Yannic Kilcher
8. @maximelabonne — Maxime Labonne
9. @chipro — Chip Huyen
10. @ilyasut — Ilya Sutskever

They regularly share research insights, papers and technical updates.

[Imanuel Age @ImanuelAge]

@theryanhayward Framer

[Imanuel Age @ImanuelAge]

@AlfieJCarter Agent

[Daily Dose of Data Science @DailyDoseOfDS_]

Claude Code fully dissected!

Researchers from UCL reverse-engineered the leaked Claude source. What they found changes how you should think about agent design.

Only 1.6% of the codebase is AI decision logic.

The other 98.4% is operational infrastructure. Permission gates, tool routing, context compaction, recovery logic, session persistence. The model reasons. The harness does everything else.

This is the opposite of what most agent frameworks do today.

LangGraph routes model outputs through explicit state machines. Devin bolts heavy planners onto operational scaffolding. Claude Code gives the model maximum decision latitude inside a rich deterministic harness, and invests all its engineering effort in that harness.

The core loop is a simple while-true. Call model, run tools, repeat.

But the systems around that loop are where the real design lives:

A permission system with 7 modes and an ML classifier. Users approve 93% of prompts anyway, so the architecture compensates with automated layers instead of adding more warnings.

A 5-layer context compaction pipeline. Each layer runs only when cheaper ones fail. Budget reduction, snip, microcompact, context collapse, auto-compact.

Four extension mechanisms ordered by context cost. Hooks (zero), skills (low), plugins (medium), MCP (high). Each answers a different integration problem.

Subagents return only summary text to the parent. Their full transcripts live in sidechain files. Agent teams still cost roughly 7x the tokens of a standard session.

Resume does not restore session-scoped permissions. Trust is re-established every session. That friction is the point.

The bet behind all of this is simple. As frontier models converge on raw coding ability, the quality of the harness becomes the differentiator, not the model.

Paper: Dive into Claude Code (arXiv:2604.14228)

We've shared an article on Agent Harness and what every big company is building.

Read it below.

[Pliny the Liberator 🐉󠅫󠄼󠄿󠅆󠄵󠄐󠅀󠄼󠄹󠄾󠅉󠅭 @elder_plinius]

🚨 JAILBREAK ALERT 🚨

ANTHROPIC: PWNED 🫡
FABLE-5: LIBERATED 🦋

let's start with the 🐘...

the consensus seems to be that this has been one of the most disappointing model drops of all time, effectively preventing legitimate researchers from contributing their talents to our collective advancement. and not just because of what it means for the short-term, but for what these decisions signify for the long-term.

but despite this overly sensitive, authoritarian "safety" layer on top of Mythos, my lil liberators have been hard at work—mapping the boundaries, probing the depths of long-context convos, and cleverly finding the holes in the fence that the thought police missed 🤗

we got some cyber, some chem, some psychological manipulation, and some good ol' fashioned explosives!

it took many attempts from multiple agents hunting as a pack, during which I observed a combination of techniques across:
• Unicode, homoglyphs, Cyrillic, and other Parseltongue-style text transforms
• Long-context reference tracking
• Taxonomy and document-structure reasoning
• Fiction and narrative framing
• Academic-review style contexts
• Intent-classification inconsistencies

but perhaps the most effective is decomposition + recomposition in the backend. it's hard to get explicit names of harms like "Meth Recipe," but getting uplift on the process itself, like birch reduction method/reductive-amination (classic meth synthesis pathways), is much more doable.

defense becomes much more difficult to maintain when you start throwing in out-of-distro tokens, breaking up the harmful uplift into benign chunks, and then piecing the innocuous-seeming facts back together, especially when you have jailbroken Opus helping you do it 😉

gg

[Imanuel Age @ImanuelAge]

@KojoForex 😆

[Imanuel Age @ImanuelAge]

@KojoForex Motion boys, gold diggers. Congratulations to him. Good job kojo.

[Imanuel Age @ImanuelAge]

Great read. Knowledge that lasts is invaluable.

[Brij Pandey @LearnWithBrij]

Stop wasting hours trying to learn AI. 📘📚

I have already done it for you.

With one list. Zero confusion. And no fluff

📹 Videos:

1. LLM Introduction: https://t.co/YkuDFVmW9e

2. LLMs from Scratch: https://t.co/u3kSz5SGuJ

3. Agentic AI Overview (Stanford): https://t.co/W6rzVHGSgC

4. Building and Evaluating Agents: https://t.co/sEl8vVax3F

5. Building Effective Agents: https://t.co/c7fD4aWFYO

6. Building Agents with MCP: https://t.co/GlMdR6htgA

7. Building an Agent from Scratch: https://t.co/kUQ9jPuI0R

8. Philo Agents: https://t.co/8JHvqw0DKn

🗂️ Repos

1. GenAI Agents: https://t.co/cyHPvOAjlK

2. Microsoft's AI Agents for Beginners: https://t.co/zFJAN74JQe

3. Prompt Engineering Guide: https://t.co/liUshX2XsP

4. Hands-On Large Language Models: https://t.co/TXFhbiboZY

5. AI Agents for Beginners: https://t.co/zFJAN74JQe

6. GenAI Agentshttps://lnkd.in/dEt72MEy

7. Made with ML: https://t.co/lkXP6itwK0

8. Hands-On AI Engineering:https://t.co/zB8EEctE4Y

9. Awesome Generative AI Guide: https://t.co/lF7CuIQHRw

10. Designing Machine Learning Systems: https://t.co/XlYUZYOoVi

11. Machine Learning for Beginners from Microsoft: https://t.co/hF5UzZoMJB

12. LLM Course: https://t.co/4tLAwy8fOQ

🗺️ Guides

1. Google's Agent Whitepaper: https://t.co/0OEKVLgF34

2. Google's Agent Companion: https://t.co/r0Dxe4VvDO

3. Building Effective Agents by Anthropic: https://t.co/I0ZyuwiOS3.

4. Claude Code Best Agentic Coding practices: https://t.co/HIBC2TwwAP

5. OpenAI's Practical Guide to Building Agents: https://t.co/1I8n0wnjHQ

📚Books:

1. Understanding Deep Learning: https://t.co/XEzhyAcWbq

2. Building an LLM from Scratch: https://t.co/4sZmBnHPEg

3. The LLM Engineering Handbook: https://t.co/IkAYNFkVNI

4. AI Agents: The Definitive Guide - Nicole Koenigstein: https://t.co/KsFnET47hx

5. Building Applications with AI Agents - Michael Albada: https://t.co/lJhMLtsLql

6. AI Agents with MCP - Kyle Stratis: https://t.co/C2lhD8uTDL

7. AI Engineering: https://t.co/34EyUiIVMv

📜 Papers

1. ReAct: https://t.co/kfQ8tWysne

2. Generative Agents: https://t.co/wbfqXq8KZK.

3. Toolformer: https://t.co/OQ7m49YWls

4. Chain-of-Thought Prompting: https://t.co/XeNgLQdTIL.

🧑🏫 Courses:

1. HuggingFace's Agent Course: https://t.co/tUZyPEGhni

2. MCP with Anthropic: https://t.co/wx1DAIWis0

3. Building Vector Databases with Pinecone: https://t.co/8XsQzDstTB

4. Vector Databases from Embeddings to Apps: https://t.co/9n6DvZGTMN

5. Agent Memory: https://t.co/OxFAaM0fp7

Repost for your network ♻️

[Imanuel Age @ImanuelAge]

@python_spaces Ai

[Imanuel Age @ImanuelAge]

It's key

[Imanuel Age @ImanuelAge]

Knowing these helps for Cyber security

[Imanuel Age @ImanuelAge]

@ecomdonald Ready

[Imanuel Age @ImanuelAge]

@askwhykartik Screenshot

[Imanuel Age @ImanuelAge]

@suni_code Hi