amalin

Detailing is important

[Camila @AiCamila_]

Eval on Shadow Traffic

A golden set is necessary. Shadow traffic shows what the golden set missed.

As a dev, I now run candidates against a shadow copy of live requests.

Shadow Eval Cheatsheet:
1. Copy a sample of live requests
2. Run the candidate in parallel
3. Score both answers
4. Never show the candidate to the user
5. Promote only after the shadow scores hold
6. Drop the shadow on any write tool

Core principle: Shadow tells you how the change behaves on traffic you did not anticipate.

Pro tip: Shadow writes are dangerous. Shadow reads only unless you have a full sandbox.

Do you ever see how a new prompt would have answered yesterday’s real questions?

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#ShadowTraffic #Eval #AgenticAI #ProductionAI #Release

[Camila @AiCamila_]

Eval Budget per Release

An eval suite that takes six hours will be skipped when the release is urgent.

As a dev, I now set an eval budget per release.

Eval Budget Cheatsheet:
1. Cap tokens and wall time
2. Run the fast suite on every commit
3. Run the full suite on promote
4. Fail if the suite exceeds the budget
5. Require justification to grow the set
6. Chart suite cost over time

Core principle: A suite people skip is not a suite.

Pro tip: Delete cases that have never failed. They cost money and teach nothing.

How long does your full eval take today, and who waits for it?

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#EvalBudget #Cost #AgenticAI #ProductionAI #Platform

[Brij Pandey @LearnWithBrij]

Stop burning tokens on Claude Code.

Use this instead 👇

A free GitHub repo (80K⭐) that turns your CLI into a high-performance AI coding system.

Link → https://t.co/c3PoO5dzfT

Why it’s different:

→ Token optimization
Smart model selection + lean prompts = lower cost

→ Memory persistence
Auto-save/load context across sessions
(No more losing the thread)

→ Continuous learning
Turns your past work into reusable skills

→ Verification loops
Built-in evals to make sure code actually works

→ Subagent orchestration
Handles large codebases with iterative retrieval

Most people think Claude struggles with complex repos.

It doesn’t.

They’re just not using the right setup.

This fixes that.

Bookmark this for your AI stack. ♻️

#AI #Claude #AIAgents #LLM #GenAI #DevTools

[Avi Chawla @_avichawla]

The next step after Karpathy's Agentic Engineering:

(built by Google)

Agent tasks can often outlive the process running them.

For instance, an agent may gather the right evidence and prepare the final answer, then stop because a person must approve the next action.

If that approval arrives a few hours later, the original process may no longer exist.

Replaying the prompt is the wrong recovery mechanism here since it runs retrieval again, may produce a different answer, and can repeat the external action.

Ideally, the system has to reopen the exact run with the same evidence and draft. It must also remember whether the final action already happened.

Google's Agents CLI lets a coding agent build this directly. It installs seven focused skills that cover ADK development, testing, and deployment to Agent Runtime.

Agents CLI Repo → https://t.co/MrlFU86tZs

(don't forget to star it ⭐ )

I tested the exact setup with a RAG agent in Claude Code.

The agent retrieves four policy passages, writes a cited customer reply, asks for approval, and exits. No worker keeps running while it waits.

A new process later opens the saved session and continues from the approval step. The retrieval count remains one, and the answer is published once.

Submitting the approval again returns `already_published` instead of calling the publication tool twice.

ADK preserves the paused run. Agent Runtime stores the session after deployment, and Agent Identity gives the deployed agent its own cloud identity.

The recording shows the whole process in action, and I worked with Google to put this together.

The app retrieves the evidence, drafts the reply, and exits with a pending approval and saved session ID. Later, the same sessions are picked up again without reinvoking the tools.

If you want to dive deeper, I've covered the complete implementation in a hands-on article, including the Claude Code prompts, duplicate-approval protection, and Agent Runtime deployment.

Read it below.

[Annatar.md @AnnatarXBT]

REA + Claude Opus 5.5 is seriously dangerous

you keep paying every month for app features your own laptop could run

REA (Reverse Engineer Anything, MIT) plugs into Opus 5.5 over MCP, runs locally and works clean-room:
> it learns what a feature does by watching it run
> nothing from the target's code lands in yours
> it writes a SPEC file from observed behaviour alone
> Opus 5.5 builds your version from that spec
> every claim comes back labelled proven, inferred or unknown

Electron, native, APK, web and .NET apps all count, as long as you own them or are allowed to examine them

one $500-a-seat tool your team lives in
one evening, and a working internal version built from a clean spec

the engineer who demos that at Monday standup ends the quarter with a new title

the full course on turning Opus 5.5 into a reverse engineering machine is below 👇

[Hamza Khalid @humzaakhalid]

Claude Code is the most unfair video editor on the internet right now: one free repo turns a 10-minute script into a finished Instagram Reel, no camera, no second app

How it works, how to set it up, and where it actually saves you the evening in my 10-page research (prompts included):

step 1 → skip the paid course: a viral video claimed Claude edited a Reel with 12 animations and 106 sound effects, then hid the guide behind a paywall. I rebuilt it from free, public parts

step 2 → meet the stack: claude-youtube-editor (free, open source) edits, Higgsfield generates, AssemblyAI transcribes, ElevenLabs voices. All of it runs in one terminal

step 3 → set up once: your script + your brand + 2 keys, and Claude Code has everything it needs

step 4 → generate the presenter: Higgsfield makes the image, the voice and a lip-synced 9:16 video at 1080x1920 through your normal account

step 5 → time every word: AssemblyAI writes a word-by-word transcript, so every cut, caption and sound cue lands on the right word

step 6 → let Claude edit: it cuts the dead air, types the captions, places the animations and drops in the sound effects, the work that used to eat your whole evening

step 7 → review the 3 checkpoints: cuts.json, the cue sheet and the preview. This is where a mistake costs credits or quietly ruins the reel

step 8 → spend zero on sound: the editor ships with 33 sound effects and 6 music beds, so most 30-second reels cost 0 ElevenLabs credits

step 9 → make reel two faster: after setup, a new reel is one /make-reel command plus a script

step 10 → know the limits: Claude edits, you approve. Something off? Fix the cut, render again, and ship an MP4 with no cut-off words and no drift

the result: writing a reel still takes 10 minutes, but editing it stops taking the whole evening, and you never have to stand in front of a camera

Learn the complete 10-page Reel Engineering blueprint, then read the full 10-step course below. ↓
https://t.co/8XB05mrMaP

[Gauri Tripathi @GauriTripa94282]

The Full AI Stack, explained in 9 levels:

① LLMs
② Inference & Access
③ Agent Frameworks
④ Tools & Protocols
⑤ Memory
⑥ Vector Databases
⑦ Embeddings & Rerankers
⑧ Data Extraction
⑨ Evaluation & Observability

Master the stack. Build better AI agents.
#AI #LLM #Tech

[Camila @AiCamila_]

Canary Eval before Full Promote

A perfect offline suite can still fail on the first thousand real users.

As a dev, I now require a canary eval before full promote.

Canary Eval Cheatsheet:
1. Shift a small percent of traffic
2. Score the canary runs
3. Compare to the baseline
4. Auto roll back on a threshold breach
5. Require a minimum sample before promote
6. Keep the canary short

Core principle: The last gate should see real users, not only recorded cases.

Pro tip: Canary the skill that writes money last. Read only skills can go first.

How much traffic do you see before a new skill version reaches everyone?

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#Canary #Eval #AgenticAI #ProductionAI #Release

[Annatar.md @AnnatarXBT]

the cleanest graph engineering setup I've seen

12,571 claims in, zero rows ever updated, and the whole index rebuilds from the log in 8 seconds

three Claude agents share one store:
one pulls claims out of raw sources, one proposes how they connect, one checks each claim against what's already there

every claim gets appended with a timestamp and a source, and nothing is ever overwritten

• the schema
> nodes.yml - entity, claim, source, artifact, run
> edges.yml - supersedes
> an ids file - one id per entity, aliases listed

• the store
> log/ - one jsonl per day, sealed when the day ends
> blobs/ - sha256 filenames, so duplicates cost nothing
> index.duckdb - derived, safe to delete any time

• the writers (.claude/agents/)
> extractor - a source in, claims out
> linker - proposes merges, never merges
> checker - supports or contradicts, always with a reason
> the write script - the only way in: validates, appends, exits

• the operations
> queries/ - every question saved as sql
> merges.pending.jsonl - a human approves each one
> make rebuild - the full index back from the log

when something's wrong, you append the correction and let the log outvote the mistake

the full road from zero to graph engineer is below 👇

[Adarsh Chetan @AdarshChetan]

20 LangGraph Concepts You Need to Know in 2026

Want to build reliable AI agents? Start with these essential concepts 👇

1️⃣ LangGraph Basics — Build stateful AI workflows.

2️⃣ Graphs & Nodes — Define tasks and connect workflow steps.

3️⃣ State Management — Track messages, inputs, and intermediate results.

4️⃣ Edges & Flow Control — Control how execution moves between nodes.

5️⃣ Conditional Routing — Route queries based on intent or conditions.

6️⃣ Tool Calling — Connect agents with APIs, databases, and custom tools.

7️⃣ Memory — Retain conversation context and user preferences.

8️⃣ Multi-Agent Workflows — Coordinate specialized agents.

9️⃣ RAG with LangGraph — Retrieve relevant context before generating answers.

🔟 Human-in-the-Loop — Add human review and approval.

1️⃣1️⃣ Checkpointing & Persistence — Save state and resume after failures.

1️⃣2️⃣ Streaming & Async Execution — Stream responses and run long tasks efficiently.

1️⃣3️⃣ Error Handling & Retries — Recover from failures with retry logic.

1️⃣4️⃣ Observability with LangSmith — Trace, debug, and monitor workflows.

1️⃣5️⃣ Subgraphs — Build reusable, modular workflows.

1️⃣6️⃣ LangChain Integration — Combine LangGraph with LangChain tools, retrievers, and prompts.

1️⃣7️⃣ Custom Nodes & Advanced Patterns — Implement custom logic, parallel execution, and advanced workflows.

1️⃣8️⃣ Evaluation & Testing — Measure accuracy, relevance, and tool performance.

1️⃣9️⃣ Real-World Projects — Combine RAG, tools, memory, multi-agent systems, and human approval.

2️⃣0️⃣ Best Practices — Design modular graphs, manage state, handle errors, and keep workflows scalable.

The big picture: LangGraph helps you build AI systems that can reason, use tools, manage state, collaborate, and recover from failures.

🔖 Save this cheat sheet for your AI engineering journey.

Follow @AdarshChetan on LinkedIn & X for more developer resources! 🚀

[Camila @AiCamila_]

Flaky Eval Detection

A case that fails every third run will eventually block a good release for no reason.

As a dev, I now detect and quarantine flaky eval cases.

Flaky Detection Cheatsheet:
1. Run the suite more than once on candidates
2. Flag cases that flip with no input change
3. Quarantine them from the gate
4. Require an owner to fix or delete
5. Track flake rate per skill
6. Alert when flake rate rises

Core principle: A flaky test is worse than no test. It trains people to ignore red.

Pro tip: Most flakes come from live tool calls or unpinned models. Mock and pin.

How many of your red builds last month were caused by a case that passed on retry?

Reply below
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#FlakyTests #Eval #AgenticAI #ProductionAI #Quality

[Gyomei @Gyome1_]

REA (Reverse Engineer Anything) is the most powerful GitHub repo I've ever used.

It's basically a legal way to hack websites and apps and steal their features, and now it runs on a free local AI with one 12GB GPU.

If you inject this config into your local model, it will reverse-engineer anything without paying for a single API call.

Steal this:

[Miles Deutscher @milesdeutscher]

REA (Reverse Engineer Anything) is the most powerful GitHub repo I've ever used.

It's basically a legal way to hack websites and apps and steal their features; I've never seen anything like it.

If you inject this prompt into Claude, it will reverse-engineer anything.

Steal this:

[Lunar @LunarResearcher]

REA + Opus 5.5 is f*cking dangerous...

Any closed-source Chrome extension can now be reverse engineered.

Down to every script and function, and you can modify how it works.

Send this prompt to Claude and reverse engineer your first Chrome extension.

Then read the full article below on how to reverse engineer anything 👇

[Hanako @hanakoxbt]

Claude Opus 5.5 can reverse-engineer any Windows .exe...

1 prompt + 10 minutes = the full map of any paid desktop app:

- Every function and what it actually does
- The licence check, found and explained
- A clean-room spec you can rebuild from

Just send this to your Claude ↓

Then read the article below on how to reverse-engineer anything:

[Francesco @francescoinweb3]

Claude is a studio, not a tool. here’s the full map on one page

most people still ask an AI for a video and hope for the best. with Claude no video model touches the frame. the animation is written as code you can read and change

> three rooms
data room: ask a question, get a chart with the query that made it
motion room: point it at a chart, get an MP4
edit room: you and Claude edit the same doc, deck or design

> the skills shelf
10 open-source repos. you need two or three, not ten
the honest pick: a taste skill (8) plus one pipeline skill

> the workflow
reference data story motion direct ship
every step leaves something you can check

> the mistakes
animating before the number is checked
describing a style instead of showing one
treating the first export as final

> your first week
one real video beats ten tutorials
you’re not generating video anymore. you’re compiling it

save the image, the full breakdown is in the article

[Camila @AiCamila_]

Model Upgrade Eval Gate

Swapping the model because the new one is cheaper is how quality silently drops.

As a dev, I now gate every model upgrade on the eval suite.

Model Upgrade Gate Cheatsheet:
1. Pin the current model in the skill
2. Run the full suite on the candidate
3. Compare accuracy, tool use, and cost
4. Require a pass or an explicit accept
5. Shift traffic only after the gate
6. Keep the old model warm for rollback

Core principle: A cheaper model is only cheaper if the answers stay good.

Pro tip: Run the gate on your hardest cases first. Easy cases hide the drop.

Can you change the model id in production without an eval result attached?

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#ModelUpgrade #Eval #AgenticAI #ProductionAI #Quality

[AI Edge @aiedge_]

Opus 5.5 can reverse engineer anything.

REA (Reverse Engineer Anything) GitHub repo is insane...

You can steal custom websites, app features - you name it.

Inject this prompt into your Claude agents:

[Gaurav Goyal @GauravGoyalAI]

AI engineers learn how to build and run a model.

But production AI needs much more.

Think of MLOps as 4 levels:

1️⃣ Foundations → Models, artifacts, Docker
2️⃣ Serving → APIs, vLLM, Kubernetes, GPUs
3️⃣ Automation → MLflow, Kubeflow, Airflow, CI/CD
4️⃣ Observability → Monitoring, drift, tracing, scaling

The real jump happens when you stop asking:

“Can I run the model?”

…and start asking:

“Can I reliably operate AI at scale?”

That’s the difference between an ML experiment and a production AI system.

Save this roadmap.

Which MLOps level are you currently at?

Follow @gauravgoyalai for practical AI & engineering insights.

#MLOps #MachineLearning #AIEngineering #GenerativeAI #LLMOps AIOps DevOps Kubernetes MachineLearningEngineering AI

[Gaurav Goyal @GauravGoyalAI]

LangChain vs LangGraph

Building AI agents? Knowing the difference between these two can save you a lot of architectural headaches.

🔗 LangChain → High-level abstractions for LLM apps, agents, tools, RAG & integrations.

🕸️ LangGraph → Stateful, graph-based orchestration for complex, multi-step and long-running agent workflows.

Think of it simply:

LangChain = Build faster
LangGraph = Control complex workflows

And yes they can work together.

If you're serious about AI Engineering & Agentic AI, these are worth learning.

Save this for your AI engineering roadmap. 📌

Follow @gauravgoyalai for practical AI & GenAI content.

#AI #GenAI #AIEngineering #LangChain #LangGraph AgenticAI LLM RAG AIAgents MachineLearning