shop GPT

[Asmit @coolcoder56]

avg salary of an enginner working at google 💀

[Cursor @cursor_ai]

Cursor Router keeps improving from millions of in-product user interactions each week.

We intelligently classify and route requests, lowering latency and reducing cost based on the task.

[Anuj @anujcodes_21]

A man spends 50 years teaching at MIT.

He knows his time is running out.

So he records one last lecture — everything he knows, distilled into a single hour.

He died 5 months later.

This is that lecture.

The most important hour you'll watch this week.

Bookmark it for later!

[Tom Dörr @tom_doerr]

Orchestrate Claude Code as a multi-agent startup team. Create comprehensive specifications first, then run parallel specialist agents to build the code.

https://t.co/bLWjm3DCzZ

[Lunar @LunarResearcher]

Ex-Google engineer just released a free 3-hour course on building and monetizing AI agents.

How to go from one agent to a full system that finds leads and makes money:

00:00 - Design an AI agent system
07:38 - Add human handoffs
19:27 - Understand RAG and vector databases
52:38 - Deploy agents to Google Cloud
1:25:57 - Turn agents into a paid WhatsApp business
1:33:00 - Convert conversations into leads
2:24:01 - Learn loops vs graphs
2:33:08 - Build a multi-tool agent graph

Most people are still building AI demos.

This course shows the full stack:

Agents → RAG → Deployment → Leads → Revenue

Building agents is the old workflow.

Monetizing agent systems is the new one.

This 3-hour watch covers more than most $500 paid courses.

Save it before everyone starts selling the same thing.

[Vyom @HelloVyom]

Another proof that you need to leave India to make it big in life.

No name college in India to CTO at OpenAI

[Param @ParamSiddh]

As an AI Engineer. Please learn

>Harness engineering, not just prompt engineering

>Context engineering, not just long prompts

>Prompt caching vs. semantic caching tradeoffs

>KV cache management, eviction, reuse, and memory pressure at scale

>Prefill vs. decode latency and why they optimize differently

>Continuous batching, paged attention, and throughput optimization

>Speculative decoding vs. quantization vs. distillation tradeoffs

>INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality

>Structured output failures, schema validation, repair loops, and fallback chains

>Function calling reliability, tool contracts, argument validation, and idempotency

>Agent guardrails, loop budgets, tool budgets, and termination conditions

>Model routing, graceful fallback logic, and degraded-mode UX

>RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness

>Retrieval evals: recall, precision, grounding, attribution, and citation quality

>Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals

>LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift

>Cost attribution per feature, workflow, tenant, and user journey not just per model

>Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries

>Multi-tenant isolation, cache safety, and cross-user context contamination prevention

>Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool

>Latency, quality, cost, and reliability tradeoffs across the full inference stack

>Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions

[sunick @Serantych]

Kimi's CEO Zhilin Yang:

"everyone's trying to build one smarter agent

many agent hits a wall fast, so instead of making it smarter we just made more - one boss, a thousand workers."

in a 39-minute talk he explains why one agent won't get you to real work.

agent swarms + long context + RL on every sub-agent - that's the fix.

bookmark this ↓

[Divya Porwal @divyaporwal_]

I’m 23 years old
– work at Google
- cracked many product based companies
– good personal portfolio
– live in a good flat
– good connections
– fulfilling my parent's dream
– helped many students get placed
– financially independent
– keep learning new skills
– travel when I can
– good balance of work & life

[codila @0xCodila]

Anthropic and Andrew Ng built an agent that uses 90% fewer tokens from scratch:

they dropped the entire book of Frankenstein into a prompt - 108,000 tokens asked one question

the input dropped from 108,000 tokens to 11

here's how:

step 1 → put everything that never changes at the top - tools, then system, then docs

step 2 → mark where the static part ends. everything above it gets cached

step 3 → one stray space breaks it - and you pay full price again

step 4 → the cache dies in 5 min - every read resets the clock

step 5 → cached tokens don't count against your rate limits. free headroom

most people never touch this - it pays for itself on day one

watch & bookmark - this 1-hour brilliant course ↓

[Amit Sharma @AmitSha77280075]

🚨 Anthropic just showed a 27-minute workshop on how to actually do prompts for Claude.

Taught by the people who built it.

Free. No registration. No paywall.

I've seen $300 courses that don't cover what they teach in the first 8 minutes.

Watch it and Bookmark it now

[Khairallah AL-Awady @eng_khairallah1]

How to become AI engineer in next 6 months:

By the end, you want to be able to:

- build LLM apps end-to-end
- use APIs from OpenAI / Anthropic / open-source stacks
- design prompts and context properly
- add tool calling and structured outputs
- deploy real projects

So, let’s discuss your roadmap month by month

Month 1: Get solid enough in coding and fundamentals

What to learn:

- Python really well
- Git + GitHub
- CLI / terminal basics
- JSON, APIs, HTTP, async basics
- basic SQL
- basic data handling with pandas
- virtual environments, package management, error handling
- FastAPI or Flask

Month 2: Master LLM app development

What to learn:

- prompting fundamentals
- system vs user instructions
- structured outputs / JSON schemas
- function/tool calling
- streaming responses
- conversation state
- cost / latency / token basics
- failure handling
- prompt injection awareness

Month 3: Learn RAG properly

What to learn:

- embeddings
- chunking
- vector databases
- metadata filtering
- reranking
- retrieval quality issues
- hallucination reduction
- citations and grounding

Month 4: Agents, tools, workflows, evals

- agent loops
- tool selection
- state management
- retries
- when NOT to use agents
- multi-step workflows
- evaluation harnesses
- task success metrics

Month 5: Deployment, product thinking, and reliability

What to learn:

- FastAPI production patterns
- Docker
- background jobs
- queues
- auth + API key security
- logging
- observability
- prompt/version management
- eval dashboards
- cost monitoring
- rate limits
- caching

Month 6: Specialize and become hireable

these knowledge and skills you gained can be applied in three directions

you need to choose one of them and focus on practice

although everything mentioned above is also best learned purely through practice

Direction 1: AI product engineer

Best if you want startup jobs fast

Focus on:

- LLM apps
- RAG
- agents
- deployment
- product UX

Direction 2: Applied ML / LLM engineer

Focus on:

- fine-tuning
- when to fine-tune vs prompt
- evaluation
- inference optimization
- open-source models
- training pipelines

Direction 3: AI automation engineer

Focus on:

- workflow orchestration
- business process automation
- multi-tool systems
- CRM, docs, email, support, ops use cases

This roadmap will help you go through a practical path, and the key is to study each of these points and then test them in real work

By month six, you will already have several built products or examples of completed tasks

And it will be much easier to get a job as an AI engineer

Save it so you don't lose it and can return to study later

[Param @ParamSiddh]

There are 2 career paths in AI:

1. The API Caller: Knows how to use an API.
(Low leverage, first to be automated, $100k salary).

2. The Architect: Knows how to build the API.
(High leverage, builds the tools, $500k+ salary).

Bootcamps teach you only to be an API Caller.

This free 17-video Stanford course trains you to be an Architect.

It's CS336: Language Modeling from Scratch.

The syllabus is pure signal, no noise: 

> Data Collection & Curation (Lec 13-14)
> Building Transformers & MoE (Lec 3-4)
> Making it fast (Lec 5-8: GPUs, Kernels, Parallelism)
> Making it work (Lec 10: Inference)
> Making it smart (Lec 15-17: Alignment & RL)

Choose your path.

[rari @0xwhrrari]

Andrej Karpathy recorded 70 minutes

Breaking down how top AI users actually work with LLMs

And most people are making it way too complicated

Worth more than most $300 AI courses

Bookmark and watch it later

[Rohit Kumar Tiwari @_rohit_tiwari_]

AI Engineering from Scratch.

503 lessons. 20 phases. 320 hours.

https://t.co/UuX9N62nNm

Phase 00: Setup & Tooling (12 lessons)
Phase 01: Math Foundations (22 lessons)
Phase 02: ML Fundamentals (18 lessons)
Phase 03: Deep Learning Core (13 lessons)
Phase 04: Computer Vision (28 lessons)
Phase 05: NLP (29 lessons)
Phase 06: Speech & Audio (17 lessons)
Phase 07: Transformers Deep Dive (14 lessons)
Phase 08: Generative AI (14 lessons)
Phase 09: Reinforcement Learning (12 lessons)
Phase 10: LLMs from Scratch (22 lessons)
Phase 11: LLM Engineering (15 lessons)
Phase 12: Multimodal AI (25 lessons)
Phase 13: Tools & Protocols (23 lessons)
Phase 14: Agent Engineering (42 lessons)
Phase 15: Autonomous Systems (22 lessons)
Phase 16: Multi-Agent & Swarms (25 lessons)
Phase 17: Infrastructure & Production (28 lessons)
Phase 18: Ethics, Safety & Alignment (30 lessons)
Phase 19: Capstone Projects (85 lessons)

[Anushka Singh @nush_1320]

A guy in my office is from IIT Bombay.

Every time he meets a girl, the first thing he tells her is that he’s from IIT Bombay.

At this point I’m not even judging, I’m just curious

Is IIT Bombay the new rizz or does bro think girls get flattered the moment they hear those two words? 😭

[aditya @adxtyahq]

for anyone asking where to learn this stuff:

• RAG → https://t.co/4bzbUIwV5g
• Agentic RAG → https://t.co/IotOiGmV1Y
• AI Agents → https://t.co/nEeMnVJQbk
• Multi-Agent Systems → https://t.co/pavDPVJEFj
• LangGraph → https://t.co/3miEqqFzF0
• LangGraph (code) → https://t.co/v7kxHZXqba
• MCP → https://t.co/lKawRb4etX
• Memory Systems → https://t.co/LSaT2UaPAS
• Evals → https://t.co/vxChxa1kqQ
• Context Engineering → search "Context Engineering Survey" on arXiv

and please skip the "build an ai agent in 10 minutes" videos
build something, watch it fail, then figure out why.

[Tom Dörr @tom_doerr]

Teaches building AI agents from first principles

https://t.co/jRSegKxOUO

[zostaff @zostaff]

Jane Street, Goldman Sachs, JP Morgan, BlackRock, Hudson River Trading, Two Sigma, D.E. Shaw.

The most expensive engineering teams in the world released their financial tools on GitHub. Here are 7 repos, one from each.

1. Jane Street, janestreet/magic-trace
https://t.co/a2G20vnewK
5.3k stars. Process tracer powered by Intel PT. When your profiler is blind, magic-trace sees every CPU instruction.

2. Goldman Sachs, goldmansachs/gs-quant
https://t.co/SMYFwP3TWD
Derivative pricing the GS traders use at their desks. MIT licensed.

3. JP Morgan, finos/perspective
https://t.co/9rgy6FxYt4
What JPM traders use to watch markets in real time. A $24k/year terminal, for free.

4. BlackRock, blackrock/lcso
https://t.co/iHwsxZDZD9
Rust optimizer for portfolio problems. Where scipy gives up, this works.

5. Hudson River Trading, hudson-trading/corral
https://t.co/YhmrQFmYaZ
Structured concurrency for C++20. The foundation of HFT infrastructure at one of the largest U.S. trading firms.

6. Two Sigma, twosigma/flint https://t.co/ebEFqcDxJ6
Time-series joins on Apache Spark with temporal tolerance. Built for billions of ticks.

7. D.E. Shaw, deshaw/pyflyby https://t.co/uYDQKtnDVd
Auto-import for IPython and Jupyter. D.E. Shaw also funded the development of IPython itself.

Bookmarked it

[AI_Explorer @ai_explorer25]

Learn AI for free directly from top companies.

1 - Anthropic:
https://t.co/cc4SxBn4S7

2 - Google:
https://t.co/r1bv5XfHsN

3 - Meta:
https://t.co/UALdoellxL

4 - NVIDIA:
https://t.co/JKTgY3mtf0

5 - Microsoft:
https://t.co/izcZcNQ9NN

6 - OpenAI:
https://t.co/nY93DIwg3G

7 - IBM:
https://t.co/lIYoazapqo

8 - AWS:
https://t.co/770qBjw9po

9 - https://t.co/4UrenSfxVZ:
https://t.co/SnPZ287LiW

10 - Hugging Face:
https://t.co/AAym51yGo9

Comment "Learning" if you find this helpful.

Repost so others can take help.

Must bookmark for future reference.