NameCan'tBeBlank

#Explorer #Pahadi #foodie

[NameCan'tBeBlank @ChaseMe02]

जो छिपा होता है उसे ही अक्सर ढूंढा करते है,
और जो सामने है उसे छोड़ दिया करते है !
#AnC @shayari @shair_shayari

[Allen Lattimer @allen_lattimer]

BREAKING: Don't copy and paste answers from ChatGPT.

ChatGPT writing is easily detectable.

Use these prompts instead and see the magic:

[Neo Kim @systemdesignone]

If you want to become top-tier with AI engineering, learn these 15 concepts (save now):

[Silent Strength @SilentStrengtth]

From Beginner to Legend: The Best Foods for Every Essential Nutrient.

1.

[Suraj Sharma @suraj_sharma14]

If you can build these 12 Agentic AI projects.

You're hired.

Project 1: Structured Output Agent
Enforce Pydantic JSON schemas, validate tool responses, retry on parse errors, log validation failures.
→ Shows: You can make LLMs reliable not random

Project 2: RAG Agent with Citation Grounding
Retrieve context, generate answers with sources, flag low-confidence responses, fallback to search.
→ Shows: You can prevent hallucinations at scale

Project 3: ReAct Planning Agent
Observe → think → act → reflect loop, max iteration limits, self-critique, graceful degradation.
→ Shows: You can build agents that don't infinite loop

Project 4: Multi-Tool Orchestrator Agent
Dynamic tool registry, capability-based routing, permission scoping, parallel execution, conflict resolution.
→ Shows: You can coordinate complex workflows

Project 5: Memory-Enabled Conversational Agent
Short-term buffer + long-term vector recall, context compression, relevance scoring, cross-session sync.
→ Shows: You can build agents that remember users

Project 6: Human-in-the-Loop Approval Agent
Uncertainty detection → pause → request human input → resume with validated context, full audit trail.
→ Shows: You can build safe, compliant systems

Project 7: Cost-Aware Agent Router
Token budgeting per task, model routing by complexity/cost, early exit on confidence, cost-per-decision analytics.
→ Shows: You can reduce infra costs by 40-60%

Project 8: Event-Triggered Automation Agent
Listen to webhooks/queues, execute workflows on triggers, idempotent execution, dead-letter handling, retry logic.
→ Shows: You can build production automation not demos

Project 9: Multi-Agent Debate System
Multiple agents propose solutions, critic evaluates, voting/consensus logic, aggregator synthesizes with confidence.
→ Shows: You can orchestrate swarms not single agents

Project 10: Self-Reflective Agent with Auto-Eval
Execute → evaluate via LLM-as-judge → critique reasoning → regenerate with constraints, log improvement metrics.
→ Shows: You can build systems that improve over time

Project 11: Production Agent with Observability
Deploy with LangSmith/Arize tracing, latency/cost dashboards, alerting on loops/failures, canary testing, rollback.
→ Shows: You can ship to production not just localhost

Project 12: Open Source Agent Framework Contribution
Extend LangGraph/CrewAI/AutoGen with new pattern, write docs + demo, publish benchmarks, submit PR + tutorial.
→ Shows: You're a community builder not just a consumer

(Bookmark this)

happy building...

[Men's Aesthetics @Emen_Aesthetics]

17 Deep Lines That Will Make You Think:

1.

[TotalBodyIQ @TotalBodyIQ]

🍹 Organ Healing Drinks 🌿❤️

[Learning Feed @LearningFeedX]

foot reflexology chart

[Up Workout @Upworkout]

Doing this alone won't get rid of your side fat—do this instead. 👍

[Succeeded Mind @SucceededMind]

If you're rebuilding at 45, start with these 15 things:

1.

[BookNote @BookNoteApp]

the 7 books to become a better student:

1) Teach Yourself How to Learn by Saundra McGuire

[TheStoicCode @X_TheStoicCode]

Un psicólogo del comportamiento afirmó que la mayoría de las personas subestima enormemente lo que pueden lograr 90 días de hábitos "aburridos".

Aquí tienes 25 hábitos sencillos que podrían hacer que seas irreconocible en solo 90 días.

[Sameer @BesuraTaansane]

These days will never return 🥹 This was real happiness 😍

clip credit @/NIIVANA.AI on IG

[HolisticHappiness | Self-Mastery® @HoliHappiness]

My wife sent 85 applications. Zero replies.

I uploaded her resume to Claude. 16 responses in 14 days.

Here are the 7 prompts I used:

[MODERN MEN MORALS @Moralityrevived]

15 HOBBIES THAT MAKE YOU DANGEROUSLY SMART:

(1) Reading → rewires how you think and see the world

[Power Beast @powerbeastt]

12 Pictures with Deep Meaning:

1.

[Mens Canvas @MensCanvas]

8 Shirt Combinations for Every Man.

1.

[Param @ParamSiddh]

Best YouTube Channels To Learn AI in 2026 (No BS)

1. Fundamentals – 3Blue1Brown
2. Deep Learning – Andrej Karpathy
3. AI Research – Yannic Kilcher
4. Practical AI – AssemblyAI
5. LLMs – AI Explained
6. ML Theory – StatQuest
7. Papers Simplified – Two Minute Papers
8. GenAI – Matthew Berman
9. AI Agents – Nicholas Renotte
10. Applied ML – Krish Naik
11. PyTorch – Aladdin Persson
12. Math for ML – Serrano Academy
13. Industry Insights – Lex Fridman
14. Real-world AI – DeepLearningAI

[Avi Chawla @_avichawla]

Prompt, context, harness & loop engineering, clearly explained!

An agent is a while loop with four layers of engineering wrapped around it:

- Prompt engineering
- Context engineering
- Harness engineering
- Loop engineering

Each one wraps the last, and the model sits in the middle, so none of them compete with the others. Instead, they just zoom one level further out.

> Prompt engineering:

This defines the input the model sees on one call, often composed of a role, instructions, examples, and an output format.

The techniques here alter the internal computation and reasoning the model goes through due to the wording it sees:

- Chain-of-thought makes it work in steps before answering
- Few-shot examples define the format and the edge cases
- A JSON schema or XML tags make the output parseable by code
- Self-consistency samples a few chains and takes the majority

> Context engineering:

It's everything the model sees on a turn, not just the prompt. That includes the query, retrieved docs, memory, prior turns, and tool outputs from earlier steps.

The window is finite and fills up fast, so the engineering work is to rank inputs and cut everything that isn't pulling weight.

You do this by:

- Retrieving only the chunks relevant to the query, then reranking them
- Keeping key facts out of the middle, where accuracy drops
- Summarizing old turns, evict stale outputs, push big blobs to files

> Harness engineering:

It's the code around the model that defines the tools, parses the calls, retries on failure, and can route work to sub-agents so one handles retrieval and another handles code.

A verifier then grades the result by running tests, validating a schema, etc.

Prompt and context involve getting one call right. The harness involves everything that has to happen around that call for it to run in a real system.

> Loop engineering:

In the usual setup, you manage the outer loop, i.e, you write a prompt, read the turns the agent runs, write the next prompt, and repeat, while catching failures.

This layer hands that job to the agent itself. It kicks off on a schedule or an event, and runs many turns with no prompt in between.

A loop inherently doesn't know when it's finished. An agent can report that it's done and halt while the tests still fail. So the stop can't be the agent's word, but rather it has to be a real signal, like:

- A turn and token cap to stop stuck runs
- A no-progress detector to catch repeated calls
- A completion check to verify the goal with a separate model or a deterministic test

By this layer, you're operating on the whole run, so the engineering moves from writing each prompt to setting the goal and the stop conditions up front and letting it run.

If you want to dive deeper into loop engineering, my co-founder wrote a full breakdown of that outer loop.

It goes from the basic while loop to a run that finishes on its own, with the code behind each part, and the parts that are hard to get right, like knowing when to stop, context rot over a long run, and keeping the checker separate from the maker.

Read it below.

[Way Of Discipline @wayofdiscipline]

DISAPPEAR FOR 15 DAYS STUDY THESE TOPICS, AND NO ONE WILL RECOGNIZE YOU ANYMORE.

[Hanako @hanakoxbt]

KARPATHY JUST KILLED THE PROMPT ERA WITH A SINGLE DOCUMENT

prompts are easy. loops are hard. and writing fifty prompts a day is the work nobody does twice.

he shifts the burden to the harness.

you define the contract once. the model writes, reviews, restarts, and reconciles. you keep judgment. it keeps the loop.

the throughline is the same in every rule: the human owns the spec and the boundary. the model owns the execution and the bookkeeping.

planner never touches code. generator never grades itself. state lives on disk, not in context.

9 rules. start with one feature, not ten. most people are still typing prompts. this turns Claude into an agent that finishes the job on its own.

here is the official document from Karpathy explaining the architecture