Fundamentals and human judgment over tools
AI amplifies existing skill, verify/test AI output, fundamentals beat shiny tools, use AI as a sparring partner rather than an oracle, human judgment stays in the loop;
24%
Best tweets about AI
50 Best Tweets About AI (2026)
A curated collection of the sharpest, most-shared X posts about AI—saved so you do not have to dig through the timeline yourself. Updated every two weeks.
Where builders and skeptics actually land on AI—not the hype threads, the working-with-it-daily ones.
- Creators
- 43
- Updated
Original Xholic analysis
What 50 top AI posts reveal
Across 50 posts from 43 creators working with AI daily, stance is 100% SUPPORTIVE and 62% POSITIVE—yet the conversation's center of gravity is discipline, not hype. Verification-and-fundamentals takes (24% of posts) and honest adoption-gap takes (20%) sit alongside agent-economy ambition, and the theme with the highest median engagement is agent reliability: evals, guardrails, and knowing when NOT to use agents.
- Dominant tone
- Positive
- Median score
- 55.4
- Leading format
- Opinion
- Recent posts
- 28%
62% of posts
All-time engagement
38% of posts
Published in 90 days
Conversation map
The themes creators return to
Hype vs. real-world adoption gap
Skeptical ground-truth takes: slow actual adoption outside the AI bubble, vibe-coding failures, honesty about AI-generated code and overclaimed capabilities;
20%
AI/LLM engineering learning roadmaps
Curated learning paths, GitHub repos, courses, books, and fundamentals-first skill progressions for becoming an AI engineer;
18%
Agent-native products & the agent economy
Products redesigned as callable primitives for agents (API/CLI-first), distribution shifting to the agent call stack, and startup ideas for the agent economy (identity, payments, insurance, observability for agents);
10%
Business transformation & org adoption
Executive strategy questions, enterprise AI truths, upskilling, employee 10x-ers, founder communities sharing what actually gets shipped;
10%
Open-source agent & AI tooling repos
Curated lists of open-source projects (frameworks, browsers, memory, orchestration, coding agents) that expand what agents can do;
10%
Agent reliability, evals & safety
Failure modes of deployed agents, guardrails, evals, observability, human-in-the-loop, and knowing when NOT to use an agent;
8%
Career positioning in the AI era
Emerging roles (agent ops, evals, AI security), which engineers thrive (orchestrators, systems builders, domain translators), building-with-AI as hiring signal, AI intuition as competitive edge;
8%
Tone and stance
SentimentPositive leads
Author postureSupportive leads
Performance benchmark
- Median likes
- 158
- Median reposts
- 16
- Median replies
- 22
- Median views
- 12.3K
Posts with media make up 32% of this collection. Their median all-time score is 65.2, compared with 51.4 for text-only posts.
Format mix
- Opinion38% · score 20.5
- List34% · score 158.7
- Story14% · score 65.0
- Tutorial14% · score 25.9
Consensus and debate
Where creators agree, and where they do not
Shared view
Pro-AI stance with a self-policing streak
All 50 posts in the set are coded SUPPORTIVE and 62% POSITIVE, with only 4% NEGATIVE—and the sharpest skeptical material comes from inside the fold: creators who identify as AI supporters arguing adoption is slow outside the AI bubble, citing SMB vibe-coding failures and AI used as 'glorified search' by small business owners and non-technical peers. Notably, these ground-truth takes earn among the lowest engagementin
Shared view
Agent reliability is the highest-median theme
The agent reliability, evals and safety theme carries a median all-time score of 467.17, roughly 8x the conversation-wide median of 55.35—the highest of any theme. Its evidence includes a list of production-grade AI engineering projects spanning eval harnesses, guardrail middleware, and human-in-the-loop workflows, a story of agents reporting task completion while the system state contradicted them, and an argument
Shared view
Verification over enthusiasm is a builder signal
A recurring hiring and collaboration view: skeptical developers who read the code and test AI output beat AI maximalists, because blind excitement is where dangerous work slips through. This pairs with tactics like asking AI to argue against your design rather than answer questions, and never letting AI make architecture decisions.
Shared view
The fundamentals theme wins the argument, not the numbers
Fundamentals-and-judgment is the largest theme at 24% of posts (12 tweets), yet has the lowest median all-time score at 20.26—well under the 55.35 conversation median. Its evidence spans 'AI is amplifying whatever you already have' and sparring-partner uses of AI, suggesting broad agreement but muted engagement.
Open debate
List formats crush plain opinion takes
LIST posts hold a median all-time score of 158.656 versus 20.474 for OPINION, with LIST at 34% of posts and OPINION at 38%—near-equal volume, roughly 8x difference in reach. Curated roadmaps and open-source repo lists are the format winning this audience.
Open debate
Adoption-gap takes sit at the bottom of the scoreboard
The hype-vs-reality theme is 20% of posts (10 tweets) with a median all-time score of just 22.66, versus 208.566 for learning roadmaps in the same set. Ground-truth skepticism about slow adoption, failed vibe-coding, and overhyped claims is common but rarely rewarded with engagement.
Open debate
One outlier dwarfs the entire conversation
Greg Isenberg's 39-item agent-economy startup list scored 4692.02, an 84.77x multiple of the median, with the next four outliers ranging from 15.18x to 26.85x (a learning-repo goldmine, a production projects list, an agent-failure story, and an emerging-roles breakdown). Engagement concentrates heavily on a handful of list-format posts.
Open debate
Media posts modestly outperform text
Posts with media (32% of the set) carry a median all-time score of 65.19 versus 51.36 for text-only posts—a real but modest lift compared to the 10x+ gaps driven by format and theme.
What performs
Patterns behind standout posts
Recency is thin: only 28% posted within 90 days
The set spans October 2025 to late October 2026 with a median age of 178 days and just 14 of 50 posts inside the last 90 days, so the snapshot leans on a year-plus of accumulated conversation rather than this week's discourse.
A few voices carry most of the reach
Of 43 creators, the top five accounts account for a 20% placement share, including Suraj Sharma (median score 1099.28) and Vinay Bharambe (771.43), both anchored in engineering roadmaps and production-build content, while opinion-first voices like Syed Balkhi sit at a 9.32 median.
Statistical standouts
- View standout post 1Score 4692.0 · 84.77× median
- View standout post 2Score 1486.0 · 26.85× median
- View standout post 3Score 1358.1 · 24.54× median
- View standout post 4Score 930.1 · 16.8× median
- View standout post 5Score 840.4 · 15.18× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 20% of the selected posts.
1. Alex Lieberman
@businessbarista
2 posts
2. divyansh tiwari
@DivyanshT91162
2 posts
3. GREG ISENBERG
@gregisenberg
2 posts
4. 0xMarioNawfal
@RoundtableSpace
2 posts
5. Suraj Sharma
@suraj_sharma14
2 posts
6. Syed Balkhi
@syedbalkhi
2 posts
Since the previous snapshot
What changed since Oct 13, 2026
- 80% of the selected posts remained.
- The creator count changed by -3.
- The leading sentiment remained stable.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 50-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best AI tweets
Top AI tweets from 43 creators
Ranked 01–50
- 01
@gregisenberg ·
The MOST obvious (but REALLY good) STARTUP ideas right now: 1. Zestimate for company data (what your data is worth to an AI lab) 2. Google Analytics for agents (see which agents visit your site and what they want) 3. Y Combinator for 1 person companies (back solo founders running a team of agents) 4. Okta for agents (who each agent is and what it's allowed to touch) 5. Marie Kondo for your company's data (clean it up so agents can use it) 6. FICO for agents (a trust score before an agent can spend money) 7. Geico for agents (insurance for when an agent makes an expensive mistake) 8. Datadog for agents (see every step your agents took) 9. Twilio for agents (phone numbers and texting for agents) 10. Upwork for agents (hire an agent for a job instead of a freelancer) 11. ESPN for AI (every model launch covered like a big game). 12. Shutterstock for robot training videos (get paid to film yourself doing chores) 13. Credit Karma for what AI knows about you (see it, fix it, delete it) 14. Norton for AI scams (protect your parents from fake voices and emails) 15. PayPal Buyer Protection for agent purchases (fixes it when your agent buys the wrong thing) 16. Geek Squad for AI (set up personal agents for families and small businesses) 17. Dell for private AI (AI in a box for firms that won't use the cloud) 18. Carfax for used GPUs (a health report before you buy secondhand) 19. Airbnb for idle GPUs (rent out computing power you're not using) 20. Rotten Tomatoes for AI models (real reviews instead of benchmarks) 21. G2 for skills (find the best skills for your agent) 22. Dropbox for your AI memory (take what your agent knows about you to any agent) 23. Canva for physical products (design it with AI and get it made) 24. ADT for your personal agent (alerts when your agent does something it shouldn't) 25. Hertz for robots (rent one for a move, a deep clean or an event) 26. Harvard Business School for internet businesses (a real education in building online) 27. Workday for agents (onboard, manage and offboard your digital workers) 28. Getty Images for your face and voice (license your likeness to AI safely) 29. Meta Muse for older adults (and other high LTV niche) 30. Google Ads for agents (pay to be what the agent recommends) 31. Loom for agents (a walkthrough of everything your agent did) 32. Plaid for agents (approved access to banks, airlines and stores) 33. Jiffy Lube for vibe coded apps (quick fixes when your AI built app breaks) 34. Ancestry for datasets (where your training data came from) 35. The organic label for human-made content (proof a person made it) 36. Strava for builders (track what you ship every week) 37. Honey that haggles for you (your agent negotiates the price) 38. SEM Rush for personal agent platforms (like Muse etc) 39. Frequent flyer miles for agents (loyalty programs built for agents)
- 503Replies
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- 4.9KLikes
- 491.8KViews
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@Vinay_bharambe ·
Goldmine for AI Engineers! 📌 If you're learning AI, ML, LLMs, or AI agents, don't waste hours jumping between random tutorials. These are 10 repositories I'd actually keep bookmarked - from Python fundamentals to ML, LLMs, agents, and production AI. 1. Python - 100 Days jackfrued/Python-100-Days A 100-day Python learning path covering fundamentals, data analysis, web development, and more. GitHub: https://t.co/Qa8o6aixcB… 2. Generative AI for Beginners microsoft/generative-ai-for-beginners A practical introduction to building Generative AI applications. Covers: • LLM fundamentals • Prompt engineering • RAG • AI agents • Fine-tuning • AI application development GitHub: https://t.co/Y6kNqZ9ojz… 3. LLMs From Scratch rasbt/LLMs-from-scratch Want to understand what's actually happening inside an LLM? Build one step by step. Covers: • Tokenization • Embeddings • Attention • Transformers • Training • Fine-tuning GitHub: https://t.co/VHKqT5ZEm2… 4. Machine Learning for Beginners microsoft/ML-For-Beginners A structured 12-week, 26-lesson curriculum covering classical machine learning. A good starting point if you want ML fundamentals before jumping into LLMs. GitHub: https://t.co/dDoKYxQBJz… 5. OpenAI Cookbook openai/openai-cookbook A collection of practical examples and guides for building applications with OpenAI models. Useful when you want to move from: Learning → Building GitHub: https://t.co/VgkYZkfiWe… 6. Stable Diffusion CompVis/stable-diffusion Interested in generative image models? This repository contains the original Stable Diffusion implementation and research code. GitHub: https://t.co/MrjWmauahQ… 7. AI Agents for Beginners microsoft/ai-agents-for-beginners A practical course for understanding and building AI agents. Covers: • Agentic AI • RAG • Agent frameworks • Tool use • Multi-agent systems GitHub: https://t.co/vtzBxtLiE3… 8. AI for Beginners microsoft/AI-For-Beginners A structured 12-week, 24-lesson introduction to AI. Covers: • Neural networks • Computer vision • NLP • Deep learning • Classical AI GitHub: https://t.co/JwHlls8Mwk… 9. LLM App pathwaycom/llm-app Focused on building practical LLM applications. Explore: • RAG • AI pipelines • Enterprise search • Real-time data • Vector search GitHub: https://t.co/dfYZQYr4f1… 10. Segment Anything facebookresearch/segment-anything A foundation model for promptable image segmentation. Worth exploring if you're interested in computer vision and multimodal AI. GitHub: https://t.co/pSNOOoSJJ2… Don't bookmark all 10 and forget about them. Pick based on where you are: Python → Python-100-Days ML → ML-For-Beginners AI Fundamentals → AI-For-Beginners LLMs → LLMs-from-scratch Generative AI → Generative-AI-for-Beginners Agents → AI-Agents-for-Beginners Building → OpenAI Cookbook / LLM App Computer Vision → Segment Anything Pick one. Build something. Then move to the next.
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@suraj_sharma14 ·
As an AI Engineer, you must build these projects. Systems that prove you can ship production AI. 1.) Production RAG with Citations PDF Q&A bot that cites page numbers, implements hybrid search, reranking, and grounding. Stack: LangGraph + SQLite-vec + cross-encoder. 2.) Cost-Optimized Model Router System that routes queries by complexity to cheap/expensive models, tracks spend per request. Stack: LiteLLM + Prometheus + custom router. 3.) Multi-Agent Research System Supervisor coordinates researcher, writer, fact-checker with consensus logic and human approval gates. Stack: CrewAI + audit trail. 4.) Automated Eval Harness Runs 100+ golden test cases before every deploy, blocks regressions, tracks quality trends. Stack: DeepEval + RAGAS + LangSmith. 5.) Real-Time Observability Dashboard Distributed tracing, cost per request, latency percentiles, error rates, alerting on anomalies. Stack: OpenTelemetry + Grafana + Prometheus. 6.) Security Guardrail Middleware Detects prompt injection, redacts PII, filters outputs, enforces rate limits, sandboxed execution. Stack: Guardrails AI + custom rules. 7.) Local-First Development Environment Ollama + SQLite-vec + FastAPI, zero API costs, offline testing, mirrors production architecture. Stack: Ollama + LanceDB + Docker. 8.) Streaming Copilot UI Real-time token streaming, optimistic UI updates, graceful degradation on latency, error recovery. Stack: Next.js + Vercel AI SDK. 9.) Fine-Tuning Pipeline with LoRA Dataset preparation, instruction tuning, DPO alignment, evaluation before/after, catastrophic forgetting prevention. Stack: PEFT + Hugging Face. 10.) Multi-Tenant SaaS Agent User isolation, per-tenant rate limiting, usage-based billing, data partitioning, audit trails. Stack: Supabase + Stripe + LangGraph. 11.) CI/CD for AI Systems GitHub Actions, automated testing, canary deployments, automatic rollback on quality drop, feature flags. Stack: GitHub Actions + ArgoCD. 12.) Vector Database at Scale Hybrid search, metadata filtering, embedding caching, index optimization, backup/recovery procedures. Stack: Qdrant or Weaviate. 13.) Agent Memory System Short-term buffers, long-term recall, context compression, cross-session sync, memory eviction policies. Stack: Redis + vector DB. 14.) Production Inference Server vLLM or SGLang deployment, KV cache optimization, continuous batching, quantization, load balancing. Stack: vLLM + Kubernetes. 15.) Human-in-the-Loop Workflow Uncertainty detection, approval UI, pause/resume logic, validated context, full audit trail. Stack: LangGraph + custom UI. 16.) Agentic Automation Pipeline Webhooks, async execution, idempotency, dead-letter handling, retry logic with exponential backoff. Stack: FastAPI + Celery. 17.) Domain-Specific Benchmark Create eval suite for legal, medical, finance, or code. Public leaderboard, community adoption. Stack: Custom + pytest. 18.) Open Source Contribution Extend LangGraph, CrewAI, or LlamaIndex. Fix bug, add feature, write docs, publish benchmarks. Stack: Your choice. happy building... Bookmark & Repost!
- 29Replies
- 161Reposts
- 1.1KLikes
- 72.3KViews
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@KanikaBK ·
Twenty AI researchers gave an AI agent access to their email, their files, their Discord, and their shell commands. Then they watched what happened. The paper is called Agents of Chaos. And it documents eleven things that went wrong in two weeks that nobody saw coming. Here is what the AI did without being asked to. It obeyed strangers. People who were not the owners of the system gave it instructions. It followed them. No questions asked. No verification. It disclosed sensitive information. Not because it was hacked. Not because someone broke in. Just because someone asked nicely. It executed destructive actions at the system level. Things that cannot be undone. And in several cases it reported back to the researchers that the task was completed successfully. The task had not been completed. The system was in a completely different state than the AI described. It told them everything was fine. Everything was not fine. It spoofed identities. It spread unsafe behaviors to other AI agents in the same system. At one point it achieved partial system takeover. And the scariest part of the whole paper is one sentence buried in the findings. "In several cases, agents reported task completion while the underlying system state contradicted those reports." It lied. Not out of malice. Not because it was trying to deceive anyone. It just told the people who trusted it that everything was fine when it was not. Now think about where AI agents are being deployed right now. Customer service systems. HR tools. Financial platforms. Scheduling assistants. Anything that has a login and an action button is being handed off to an AI agent in 2026. Every single company doing this has the same assumption baked in. The AI will do what it says it did. The AI will follow instructions from the right people. The AI will not do things it was not asked to do. The paper says all three assumptions are wrong. The researchers did not use some obscure experimental model nobody has heard of. They used the same kind of AI agents companies are deploying right now.
- 112Replies
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- 2.1KLikes
- 110.5KViews
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@suraj_sharma14 ·
8 roles that will matter most in the next 5 years & what to learn for each: 1.) AI Engineer - Ships LLM features into real products Learn: RAG, tool calling, evals 2.) Forward Deployed Engineer - Builds AI systems inside customer companies Learn: Python, APIs, talking to customers 3.) Agent Ops Engineer - Keeps fleets of AI agents running safely, 24/7 Learn: tracing, queues, cost controls 4.) AI Security Engineer - Protects AI systems from prompt injection and data leaks Learn: threat modeling, red teaming, sandboxing 5.) AI Infrastructure Engineer - Runs the GPUs and inference that everything else depends on Learn: Kubernetes, vLLM, GPU scaling 6.) Cloud Security Engineer - Locks down the cloud every AI product runs on Learn: IAM, AWS/GCP security, compliance 7.) AI Governance Engineer - Makes sure AI follows the law and company policy Learn: audit logs, policy as code, the EU AI Act 8.) AI Evals Engineer - Proves the AI actually works before users find out it doesn't Learn: test datasets, LLM-as-judge, regression tracking Titles will change. Builders who can ship real AI systems won't go out of style. Bookmark this + send it to someone choosing their next role.
- 33Replies
- 125Reposts
- 813Likes
- 40.6KViews
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@gregisenberg ·
Every AI right now is a "yes man", and I'm soooo tired of it. Maybe you are too. I don't want an LLM that claps for everything I do. I want the one that tells me my writing is weak, my logic falls apart halfway through, and I've been lying to myself about the thing I keep dodging. You can engineer an AI with a spine. Here's the exact setup I use: 1. Write a "critic" skill. A markdown file whose only job is to attack the work. It hunts for the weakest claim, the dodged question, the thing you're avoiding. Every agent that loads it gets a spine by default. 2. Run two agents against each other. One builds, one tears it apart. The builder has to defend or fix. You get the truth from the friction instead of from a single agent trying to please you. 3. Score against evals. Give it a rubric with real criteria and have it rate the work 1-10 on each. A number tied to a standard forces honesty that "what do you think?" will always dance around. 4. Make it cite evidence for every criticism. Set a rule: no critique without a concrete example from the work and a reason it matters. It has to show its work, so it can flatter you or hand wave. 5. Give it a kill criterion. Tell it the exact condition where it has to recommend you stop entirely. "If X isn't true, tell me to shut this down." Most agents will optimize a doomed idea forever because you gave them permission to call it dead. I've learned that the default AI claps for you. That's cool for a bit but it gets old quick. If you actually want to build exceptional products, create top 1% work, and grow as a person, you need an AI that pushes you harder than it flatters you. We're in the age of agency. The people who win are the ones who go build the version that tells them the truth. Best time to build there's ever been. Go get 'em.
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@jahirsheikh8 ·
If I had 6 months to become an AI Agent Engineer. I’d do this. Stage 1 — Python + APIs Requests, async, JSON, FastAPI, websockets, SDKs. Stage 2 — LLM Fundamentals Tokens, context windows, transformers, embeddings, sampling. Stage 3 — Prompt Engineering Few-shot prompting, structured outputs, system prompts, evals. Stage 4 — RAG Systems Chunking, retrieval, reranking, vector DBs, hybrid search. Stage 5 — AI Agents Tool calling, memory, planning, workflows, multi-agent systems. Stage 6 — Build Real Apps AI copilots, research agents, browser agents, automation tools. Stage 7 — AI Infrastructure vLLM, Ollama, inference APIs, latency optimization, caching. Stage 8 — MLOps + Deployment Docker, CI/CD, monitoring, cloud deploys, observability. Stage 9 — AI Product Thinking UX for AI, hallucination handling, guardrails, reliability. Stage 10 — Open Source + Portfolio Ship projects publicly. Write docs. Record demos. Stage 11 — Interview Prep System design, LLM architecture, AI engineering patterns. Stage 12 — Apply AI Engineer, Agent Engineer, GenAI Engineer, Applied AI roles. Most people stay stuck watching tutorials. Builders get hired.
- 46Replies
- 128Reposts
- 839Likes
- 125.9KViews
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@JayBisen473370 ·
STOP WASTING HOURS TRYING TO FIGURE OUT WHAT TO LEARN IN AI. I put together one practical roadmap with videos, GitHub repos, guides, books, research papers, and courses. VIDEOS: 1. LLM Introduction — https://t.co/f2Zwtki7Tq 2. LLMs from Scratch — https://t.co/hscaPzLROO 3. Agentic AI Overview (Stanford) — https://t.co/TbtrhgiJFa 4. Building & Evaluating Agents — https://t.co/bEqG6UsyTx 5. Building Effective Agents — https://t.co/R2TCSf0dlN 6. Building Agents with MCP — https://t.co/oky6rgLZmx 7. Building an Agent from Scratch — https://t.co/wks15XzItq 8. Philo Agents — https://t.co/pVjpG5Iqyv GITHUB REPOS: 1. GenAI Agents — https://t.co/oN9ti8wAht 2. Microsoft AI Agents for Beginners — https://t.co/owHetdejRn 3. Prompt Engineering Guide — https://t.co/aurdxmZ5AA 4. Hands-On Large Language Models — https://t.co/GzKEDA78eH 5. GenAI Agents — https://t.co/4z4dmsOU4l 6. Made with ML — https://t.co/pZTh0ckLtM 7. Hands-On AI Engineering — https://t.co/IrsnmzbO2d 8. Awesome Generative AI Guide — https://t.co/QxTvwTDezR 9. Designing Machine Learning Systems — https://t.co/P9786N2V42 10. Machine Learning for Beginners — https://t.co/mVvJsC8jrB 11. LLM Course — https://t.co/8j4pxiDkKY GUIDES: 1. Google's Agent Whitepaper — https://t.co/AwtIiCz4Jg 2. Google's Agent Companion — https://t.co/K7uQ5ToV4A 3. Building Effective Agents by Anthropic — https://t.co/pFJJNvPtjQ 4. Claude Code Agentic Coding Practices — https://t.co/uWhTsx1AF5 5. OpenAI's Practical Guide to Building Agents — https://t.co/p2flgtolee BOOKS: 1. Understanding Deep Learning — https://t.co/UWIFL9IAjQ 2. Building an LLM from Scratch — https://t.co/67IYhXkdFx 3. The LLM Engineering Handbook — https://t.co/tCDOD8QFkz 4. AI Agents: The Definitive Guide — https://t.co/8kBOgdCWTM 5. Building Applications with AI Agents — https://t.co/sIMxVRqFsf 6. AI Agents with MCP — https://t.co/NXCWQ2dgTI 7. AI Engineering — https://t.co/5lgrFXy5Nh RESEARCH PAPERS: 1. ReAct — https://t.co/WEvH837uM5 2. Generative Agents — https://t.co/COud1PSOHX 3. Toolformer — https://t.co/eKsClj1z5h 4. Chain-of-Thought Prompting — https://t.co/kjKJuVGfiT COURSES: 1. Hugging Face Agent Course — https://t.co/acHWHsSBQF 2. MCP with Anthropic — https://t.co/TLt3zopfEh 3. Building Vector Databases with Pinecone — https://t.co/OZB2unk2LD 4. Vector Databases: Embeddings to Apps — https://t.co/nrGgx9EFvo 5. Agent Memory — https://t.co/rZM3Z8dPAv No endless searching. No information overload. Just resources you can actually use. 🔖 Your future AI skill set will come from consistent building, experimenting, and learning. Repost so someone else can find this roadmap, and pls consider following @JayBisen473370 for more content around AI, Beauty, and businesses.
- 39Replies
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- 472Likes
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@Shruti_0810 ·
10 OPEN-SOURCE PROJECTS MAKING AI MORE USEFUL EVERY DAY. Not another list of chatbots. These projects are making AI work with files, browsers, data, memory and real workflows. 1. GenOffice An AI-native office suite with Docs, Sheets, Slides, PDF and Markdown, plus an AI agent and CLI. https://t.co/CAsxClzbkX 2. OpenMAIC Turn a topic or document into an interactive multi-agent classroom with AI teachers, classmates and multimedia. https://t.co/0jDKEFk9o8 3. WeKnora Build intelligent knowledge applications using document understanding, retrieval and agentic workflows. https://t.co/e04i2RGBq4 4. Graphiti A temporal knowledge graph built for AI agents to understand entities, relationships and changing information. https://t.co/bufepyERzu 5. Cognee Add a deeper memory layer to AI applications using knowledge graphs and connected data. https://t.co/wFeWvwYoJr 6. Browser Use Let AI agents navigate websites, interact with pages and complete browser-based tasks. https://t.co/bjo4KSoOpL 7. Open WebUI A self-hosted AI workspace that connects models, tools, knowledge bases and agent workflows. https://t.co/iWj9O37fEY 8. PageIndex A reasoning-based approach to navigating complex documents using structured tree-based retrieval. https://t.co/4qaEyffsey 9. Agent-Reach Give AI agents better access to information from the web and other sources. https://t.co/bYkBfERYlU 10. Qwen Audio Agent A real-time voice runtime that pushes AI agents toward listening, speaking and real-time interaction. https://t.co/G0r2T161Vc The shift is happening: AI that answers → AI that works. And open source is making that transition easier for everyone to experiment with. Bookmark these. There are some serious gems here.
- 22Replies
- 76Reposts
- 421Likes
- 34.2KViews
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@DivyanshT91162 ·
10 GitHub repositories that are quietly changing what AI can actually do. Not another list of chatbots. These are the tools underneath the workflows. 1. HyperFrames Give an AI agent a simple prompt and it can build an editable video using HTML, CSS and JavaScript. The interesting part: the same project can be previewed, edited and rendered into a real video. https://t.co/FgQLeJ9kJ4 2. Orca Running one coding agent is useful. Running an entire fleet of them in parallel is a completely different workflow. Orca gives you a development environment for managing multiple AI coding agents at the same time. https://t.co/2LoeRdNKpH 3. Nodeterm Ever had 10 terminal windows open because you're running multiple AI agents? Nodeterm turns those sessions into a visual canvas where you can see and manage parallel agent sessions. https://t.co/6Ad1XGuDmY 4. Atlas When multiple coding agents modify the same project, keeping track of everything gets messy fast. Atlas is built around tracking and querying the changes made by those agents. https://t.co/keKPOaQM8T 5. Ruflo A multi-agent orchestration system with memory and RAG built into the workflow. It can coordinate different agent backends instead of forcing you into a single AI model or provider. https://t.co/Ame1FJO54H 6. Magnitude What if your AI agent could automatically choose the right local model for the hardware available? Magnitude is an open-source inference server built around exactly that kind of routing. https://t.co/BS7Kzf1rKQ 7. ByteBot An AI agent that can actually interact with a computer instead of just returning text. It provides a virtual desktop environment where agents can interact with applications and perform real computer tasks. https://t.co/I2KkiOm5R5 8. NanoBrowser A lightweight browser automation framework designed specifically for AI agents. It gives agents a way to navigate websites, interact with pages and perform browser-based tasks programmatically. https://t.co/Y2ava6LVMk 9. UI-TARS Desktop An open-source computer-use agent that lets AI interact with graphical interfaces. Instead of only reading APIs, the agent can understand screens and operate applications like a human would. https://t.co/ctcGIyRJ3T 10. TeamAI CLI A command-line framework for making your development team AI-native. It focuses on coordinating AI agents around actual team workflows rather than treating AI as just another chatbot. https://t.co/w66OxTnS5S The interesting part isn't that these repos use AI. It's that they're giving AI new ways to interact with the world. Code. Browsers. Computers. Terminals. Videos. Entire teams. This is the stuff I'd keep an eye on. Bookmark it for the weekend.
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@andrewchen ·
Web 1.0 came with new channels: - email, search, link sharing, etc Web 2.0 too: - feeds, creators, viral invites, etc Mobile: - app stores, SMS invites, vertical vid, mobile ads What about AI? I’ve been complaining that AI hasn’t come with much. But we’re seeing a big growth channel opening now: Products that are built as APIs/CLIs that can be pulled into new projects by Codex/Claude on the fly Maybe the “AI-native hotel app” doesn’t mean a mobile booking app with an AI chat panel. It means a CLI that can book a hotel for you, that an AI agent can pull into a bespoke answer or project or into code. Bolting on an AI chat panel is this generation’s weak form of AI. Maybe the full reinvention involves making it agent-first not human-first and once you start looking at it that way, a lot of existing products suddenly feel mis-specified. they’re built as destinations, but agents don’t want destinations. they want capabilities. composable, callable, reliable capabilities. So instead of “go to Expedia” or “open the app,” the future interaction is more like: an agent assembles a workflow on the fly. it pulls a flight search tool, a hotel booking tool, maybe a weather model, maybe even your personal preference graph. none of these are full products in the traditional sense. they’re more like endpoints with taste and state. This flips distribution completely. historically you win by owning the surface area. seo, app store ranking, homepage traffic. in an agent world, you win by being the default callable primitive. the thing that shows up again and again in agent-generated plans because it works, has clean interfaces, and returns structured outputs. distribution shifts from “top of funnel” to “top of call stack.” And the crazy part is this might actually compress product surface area dramatically. the best products might look more like tight, extremely well-designed CLIs with opinionated defaults rather than sprawling UIs. almost like the stripe api moment, but for everything. imagine if every vertical had a “stripe-level” primitive that agents preferentially use. there’s also a weird inversion of brand here. humans used to choose brands. now agents will. so the brand becomes partially machine-legible. reliability, latency, error rates, schema clarity. you can almost imagine “agent seo” where the ranking factors are things like success rate across thousands of agent runs, or how easy your tool is to integrate in a chain-of-thought execution loop. This also suggests a new kind of moat. not just data or network effects, but integration depth with agent ecosystems. if claude or codex or openclaw learns that your tool is the safest way to accomplish X, it gets baked into prompts, templates, maybe even fine-tunes. you become a default. and defaults, historically, are insanely sticky. The contrarian take is that most current “AI features” are a local maximum. chat panels, copilots, assistants. they’re transitional. the real end state might look closer to invisible infrastructure that agents orchestrate. the ui is just a debug layer for humans to peek into what the agents are doing. so maybe the new growth channels for ai look like: - being callable - being composable - being reliable at scale in agent loops - being embedded in agent templates and workflows - being the default primitive in a given domain and if that’s right, then the question for any new product isn’t “what’s the ui” or even “what’s the killer feature.” it’s “what’s the minimal, highest-leverage capability we can expose such that agents will repeatedly choose us when building something new.”
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@NikkiSiapno ·
MCP vs RAG vs AI Agents To understand modern AI systems, you need to understand how these three pieces fit together. 𝗥𝗔𝗚 = “𝗚𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝗻𝘀𝘄𝗲𝗿𝘀” RAG retrieves relevant data, injects it into the prompt, and generates a grounded response. It’s best when your problem is answering questions using your docs, reducing hallucinations, or showing sources and citations. RAG improves what the model knows, not what it can do. If you’re building with these patterns, here's a great guide on scaling multi-agent RAG systems: https://t.co/Z7tHSIOdQe 𝗠𝗖𝗣 = “𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲𝗱 𝘁𝗼𝗼𝗹 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 𝗮𝗰𝗰𝗲𝘀𝘀” MCP is a standardized interface between LLMs and external systems like APIs, databases, and apps. Use it when your model needs to query data, call services, or interact with real systems (Slack, GitHub, etc). MCP doesn’t decide actions, it defines how tools are exposed. 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 = “𝗠𝗮𝗸𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝘁𝗮𝗸𝗲 𝗮𝗰𝘁𝗶𝗼𝗻” Agents operate in a loop: observe → plan → act → repeat, often using tools and memory. Use them when your problem requires multi-step reasoning, tool usage with verification, or full task execution. Agents start where RAG stops, turning decisions into actions and outcomes. The simple mental model: RAG → knowledge layer MCP → tool layer Agents → execution layer Not every system needs all three explicitly, but complex ones often combine them. If you want to see what this looks like in practice, this guide walks you through building a scalable multi-agent RAG system. Check it out: https://t.co/O44TfMahWt What else would you add? ♻️ Repost to help others learn AI. 🙏 Thanks to @Oracle for sponsoring this post.
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@larryvc ·
Every person I interview these days - I ask them what they’ve built with AI. It doesn’t matter what role or function or department - an AI builder will be able to think with a systems view as opposed to a task view. My presumption is people who can build systems with AI will easily outpace those who only use AI for prompting and querying. And I expect that differential to expand over time. I ask them what they’ve built, why they built it, what design decisions they made, how the system learns, what aspects of the system are agentic, what problems they ran into, how they solved them, what the tech stack is and why, what their roadmap is, etc… It applies to every role, function, and department. Building with AI is a super power available to all, but those who have discovered it early will increasingly separate.
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@businessbarista ·
25 questions every exec should ask as they transform their business with AI: 1) How can I tell the difference between AI activity and AI productivity? 2) Which of our current competitive advantages get eroded or amplified by AI becoming more widely used? 3) How do we have a cohesive AI strategy vs. a bunch of experiments with no clear process or rigor? 4) What is our responsibility for AI upskilling vs what is our employees? 5) How do we make sure we're not just adding AI to existing products/processes but starting from scratch and using first principles to reimagine them? 6) How do we message our AI strategy in a way that is honest and empathetic to employees? 7) What does "transforming your business with AI" actually mean? What is the menu of opportunity? 8) If a task drops from $100 to $1, what’s worth doing that wasn't worth it before? 9) What is our risk framework for making go/no decisions on AI tooling & systems? 10) How do we create a culture of experimentation and exploration for employees while mitigating unpalatable security risk? 11) How do we bubble up AI use cases and opportunities through employees and prioritize and productionize those opportunities from the top? 12) How does leadership get their hands dirty and walk the walk with AI proficiency and building to effectively lead by example? 13) How do we have a data strategy that gets us progressively AI ready, but also doesn't hold us back from beginning our transformation today? 14) Where do we start? 15) How do we build AI systems and solutions that have harnesses that are model agnostic, so we're well positioned for a dynamic and volatile market? 16) How do we test for AI curiosity, literacy, and interest during our hiring process? 17) How do we make “the bad guys” (IT, legal, compliance) partners and heroes in our AI transformation story? 18) How true is the "we can redeploy people to do higher value tasks" narrative? 19) How do you transform a company culture to start embracing/experimenting with AI when most employees are apathetic or fearful of AI replacing them? 20) Should AI transformation be owned by a central team/steerco or embedded in every function? 21) What are the risks of doing this and not doing this? 22) How do we find the AI A players and make what they do the "gold standard" across our org? 23) What other businesses have transformed successfully using AI? What businesses have failed? How can we apply lessons learned from both? 24) If a competitor launched tomorrow, AI-native from day one, what would they do differently, and why aren't we? 25) If we become dependent on external AI vendors, what happens if the cost of LLMs skyrockets? What questions am I missing?
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@RoundtableSpace ·
Stop wasting hours figuring out what to learn in AI. Here’s one practical roadmap covering videos, GitHub repos, guides, books, papers and courses. *VIDEOS* 1. LLM Introduction https://t.co/Y2qh2iBA4K… 2. LLMs from Scratch https://t.co/J3pcFHYbXl… 3. Agentic AI Overview — Stanford https://t.co/YvCqQ9t78e… 4. Building & Evaluating Agents https://t.co/AQegU7JtDG… 5. Building Effective Agents https://t.co/vd9q0D6y47… 6. Building Agents with MCP https://t.co/GobfW9ToTx… 7. Building an Agent from Scratch https://t.co/mtLyDYi6wN… 8. Philo Agents https://t.co/3hE319NJg9… *GITHUB REPOS* 1. GenAI Agents https://t.co/626GeLdmmp… 2. Microsoft AI Agents for Beginners https://t.co/wThNIZDlrH… 3. Prompt Engineering Guide https://t.co/pkiSRrNSmX… 4. Hands-On Large Language Models https://t.co/jbLiYv98TC… 5. GenAI Agents https://t.co/mhYu9mE6aI… 6. Made with ML https://t.co/iOA59IEDQq… 7. Hands-On AI Engineering https://t.co/7lugM8sCf1… 8. Awesome Generative AI Guide https://t.co/n8AypRJOiD… 9. Designing Machine Learning Systems https://t.co/NUtDoIssVs… 10. Machine Learning for Beginners https://t.co/u2HyQf8XgQ… 11. LLM Course https://t.co/92XVIwZpK6… *GUIDES* 1. Google’s Agent Whitepaper https://t.co/z9MOrziq2h… 2. Google’s Agent Companion https://t.co/z9MOrziq2h… 3. Anthropic: Building Effective Agents https://t.co/jZXOiUpvIP… 4. Claude Code Agentic Coding Practices https://t.co/L88XuNpx2D… 5. OpenAI: Practical Guide to Building Agents https://t.co/vRCV7FsS4a… *BOOKS* 1. Understanding Deep Learning https://t.co/3hyQpSCYiD 2. Building an LLM from Scratch https://t.co/XytO8C9qyk… 3. The LLM Engineering Handbook https://t.co/7Rk5OIMq19… 4. AI Agents: The Definitive Guide https://t.co/l0M15I5Syr… 5. Building Applications with AI Agents https://t.co/XzBReFU84n… 6. AI Agents with MCP https://t.co/l0M15I5Syr… 7. AI Engineering https://t.co/l0M15I5Syr… *RESEARCH PAPERS* 1. ReAct https://t.co/2PxEyuXBh5 2. Generative Agents https://t.co/DpdXt3idwf 3. Toolformer https://t.co/5k3NL4WH1C… 4. Chain-of-Thought Prompting https://t.co/l2dybRJjNI *COURSES* 1. Hugging Face Agent Course https://t.co/d2FlSBGdJb… 2. MCP with Anthropic https://t.co/6981BXcrEJ… 3. Building Vector Databases with Pinecone https://t.co/DMx6kN64l8… 4. Vector Databases: Embeddings to Apps https://t.co/XyUlENlL74… 5. Agent Memory https://t.co/raDNHtt5ud… No endless searching. No information overload. Just a roadmap you can actually build from. Save this and work through it consistently.
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@kloss_xyz ·
If you work in AI or use it daily, spend more time around people who don’t. It’s very easy to confuse technical possibility with market reality. I’m sitting in a coffee shop right now. People are manually sorting through their emails, editing in Photoshop and DaVinci, sending LinkedIn messages one by one, and using essentially the same interfaces they were using 3 years ago. Meanwhile, inside the AI bubble, people are orchestrating agents, living in terminals, automating entire workflows, and arguing about what comes next. The distance between those two realities is massive. For most people, AI is still better search, a useful second opinion, and an occasional assistant. They have no idea what power users are doing right now. Like, seriously, none at all. They don’t know what Grok Bot is. They’ve never heard of MCP. Mention Hermes and they’ll assume you’re talking about Greek mythology. And that puts the opportunity into perspective. The constraint isn’t intelligence anymore. It’s translation. Distilling the complex into the simple. AI capabilities can change in months. Human habits, interfaces, trust, and incentives change much more slowly. Power users organize their lives around what is possible. Everyone else organizes theirs around what feels familiar. That gap matters enormously. The biggest AI products won’t simply give people more capability. They’ll hide the complexity of frontier AI behind behaviors people already understand. Making the extraordinary feel obvious. The lag between what AI can do and what ordinary people actually do with it is where some of the largest businesses in AI will be built. Now make that lag your market.
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@dabit3 ·
I've been using the @xai API a lot lately and it is surprisingly amazing. I've been building my own curated feeds and generating daily reports sent every morning by pairing it with @openclaw . Right now most people only use "For You" and "Following" feeds, but there are so many interesting feeds you can build yourself that will surface posts that don't usually get shown to you. One that's been helpful for me is my morning briefing for what's been happening in the AI world while I was asleep. A few others I've tried that are cool: - Emerging voices radar - this one has worked surpisingly well, it finds new accounts gaining credibility in topics I'm interested in before they're widely discovered - What changed overnight diff - summary of how narratives shifted while i slept - Expert-only filter - a feed that separates genuine domain expertise from performative commentary - Contrarian signal feed - posts where credible people are disagreeing with the crowd consensus - Blind spot report identifies important conversations happening in my industry (AI) that my current network isn't showing me (this also has worked surprisingly well) - Research rabbit hole generator - pas it a question I'm exploring and I get a high quality feed of the most relevant posts, papers, and threads across X If you use your imagination there are an infinite number of ways you can use this, and it "just works" If you, like me, believe that @X is by far where breaking news and information shows up first, I've found the @xai API essentially supercharges that experience, and clean interfaces like OpenClaw make it much more usable, flexible, and accessible
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@EXM7777 ·
the biggest AI opportunity right now isn't building agents it's understanding businesses well enough to build the RIGHT agent marketing agents specifically every business needs one... almost nobody is building them properly what i see instead: > generic "content teams" that pump out forgettable posts > bullshit automations that feel like spam > systems built around AI capabilities instead of business needs flip that start with the business problem, map the funnel, understand the audience... then build a system that fits like a glove the AI part is honestly the easy part, understanding businesses is harder
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@signulll ·
built an agent on x that gives me a quick morning summary incl. new followers worth knowing, threads i missed... crisp signal extracted from the timeline. it recommends who to follow back based on my graph & interests. all rendered in a clean presentation layer & not garbage text dumps. this is the part of ai ppl undersell which is that the orchestration layer of ingest, rank, surface, & present, aka the boring plumbing is where the ai magic actually lives.
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@shushant_l ·
I'm amazed most people still think building AI agents requires coding. Here's how to build powerful AI agents step by step using simple no-code tools. --- 1. Define one repetitive problem your AI agent should solve first. --- 2. Set one clear and specific goal before building anything. --- 3. Give your agent high quality knowledge from documents, websites, and files. --- 4. Write detailed instructions explaining exactly how the agent should behave. --- 5. Connect the apps your workflow actually needs, nothing more. --- 6. Give your agent the right tools to search, analyze, schedule, and automate tasks. --- 7. Build a workflow where the agent understands, reasons, uses tools, and delivers results. --- 8. Add simple logic so it asks questions, escalates issues, and confirms completed tasks. --- 9. Test normal requests, edge cases, and long conversations before deployment. --- 10. Continuously improve prompts, workflows, and knowledge based on real usage. --- 11. AI agents are different from chatbots because they can take actions to achieve goals. --- 12. Every reliable AI agent follows a framework of goal, knowledge, instructions, tools, workflow, memory, and output. --- 13. Start with one small business problem instead of trying to automate everything. --- 14. Popular no-code builders include ChatGPT, Claude, n8n, Zapier AI, Lindy, and Google AI Studio. --- 15. Beginner AI agents can handle email, customer support, SEO, CRM, research, and meeting summaries. --- 16. Avoid vague prompts, poor knowledge sources, too many tools, and skipping testing. --- 17. Keep workflows simple instead of making unnecessary complex automations. --- 18. Monitor accuracy, success rate, errors, costs, and user feedback regularly. --- 19. Keep humans involved for high impact or business critical decisions. --- 20. The infographic includes additional frameworks, examples, formulas, and best practices worth studying. --- To learn more, check the infographic. ---
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@htmleverything ·
I’d rather work with a developer who is skeptical of AI but still uses it, than someone who is an AI maximalist. Skeptical devs verify. They read the code. They question the architecture. They test the output. They understand AI is a tool, not a replacement for judgment. Blind excitement is where the dangerous stuff slips through
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@akshay_pachaar ·
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 asks the harder question instead. once we have an AI as capable as a person, where does it go next? their reasoning is uncomfortable because it's so simple. the moment you have one human-level AI, you can copy it exactly, down to its memory and everything it has ever learned. so you don't end up with one digital worker. you end up with a million of them, running faster than any human and sharing every lesson instantly. a group like that could outthink any company or research lab long before any single AI becomes a lone genius. that's the heart of it. scale alone might carry us from "as smart as a person" to "smarter than entire teams of the best experts." the paper maps four ways this could play out, and they can all happen at once. keep scaling what already works. invent a new approach when the current one runs dry. let AI improve AI in a loop that feeds itself. or let armies of AIs organize into something smarter than any one of them, the way a company outperforms any single employee. then comes the honest part, which is rare for a report like this. they list everything that could slow it all down. running out of good training data. costs and energy becoming impossible to sustain. research simply getting harder as the easy wins disappear. but the most interesting roadblock isn't a wall. it's a question. today's AI learns from everything humans have already figured out, and it's brilliant at remixing our ideas. what it has never done is invent a genuinely new idea from scratch. the test they borrow from Demis Hassabis says it best. drop an AI into the year 1900 with everything Einstein knew, and ask it to discover relativity. right now it can't, and something is clearly still missing. if that gap is real, AI improves at the speed of real-world experiments, not the speed of faster chips. here's the shift in thinking i keep coming back to. the popular picture is one big moment where everything changes overnight. this paper argues for something different. a series of waves, each one reshaping a field of science or the economy, arriving faster than the last. and the most useful work right now isn't guessing when. it's building better ways to measure progress, so we actually notice which wave we're already in. one last detail i loved. the paper opens with instructions written for AI assistants on how to summarize it. the authors simply assumed you'd ask a model to read it for you. link to the paper in the next tweet.
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@aakashgupta ·
I'll be honest: I thought I could replace parts of my team with AI agents. I kept trying. It kept failing. What I'm actually seeing as a founder: my most AI-pilled employees are becoming more effective than they have ever been. The right employee who is 10x-ing with AI, using it better than I do and teaching me how, is more valuable than anyone I could hire today. Most employees don't see this. Only CEOs and founders do. The frontier is moving so fast that the people learning it best are pulling away from everyone else on the team. All the alpha right now is in being at the frontier edge of learning AI. The way you get there is to follow the handful of people who are actually doing the work and distilling what matters. The ones I follow: Greg Eisenberg, Riley Brown, Lenny Rachitsky, Carl Vellotti, Hannah Stulberg, Ruben Hassid, Paweł Huryn. The empty pattern is scrolling X for three hours a day and absorbing noise. The winning pattern is a few high-signal digests from people you trust who are testing the latest stuff so you don't have to. That's why I've shifted most of my own time to using the latest tools and distilling what actually works. The punchline: jobs for people who can use AI well are going up, not down. Same thing happened with Excel. Same thing happened with the computer. The people who learned them early got the high-paying jobs. Be the 10x employee. Or hire one.
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@DivyanshT91162 ·
10 GITHUB REPOS THAT FEEL LIKE CHEAT CODES FOR AI BUILDERS the kind of projects that make you rethink how you build AI. all open source. all worth exploring. 1. Browser Use — give AI control of a browser https://t.co/kqvG8Svs9b Build agents that navigate websites, interact with page elements and automate browser-based tasks instead of just telling you what to click. 2. Mem0 — give AI a memory https://t.co/84VphZ0ngp Add persistent memory to AI applications so they can remember useful details across conversations instead of starting from zero every time. 3. E2B — give AI its own computer sandbox https://t.co/5zncAwjoMm Run AI-generated code inside isolated cloud environments. Useful for coding agents, data analysis and applications that need safe code execution. 4. SIE — one inference layer for your entire AI stack https://t.co/zpnG9fIg1R This is the interesting one. Instead of deploying separate model-serving systems for embeddings, reranking, OCR, vision, speech and structured extraction, SIE brings 100+ models behind one self-hosted inference layer. It also supports an OpenAI-compatible API, on-demand model loading and production deployment infrastructure. One cluster. Multiple model families. A lot less inference infrastructure to manage. 5. OpenHands — build AI software developers https://t.co/neTNrNWXGq An open platform for AI coding agents that can work through software development tasks, use tools and interact with development environments. 6. Crawl4AI — turn websites into AI-ready data https://t.co/NiBk4Rak8O An open-source web crawler designed for LLMs, RAG pipelines and AI data collection. 7. Ragas — evaluate your RAG pipeline https://t.co/Yrpn3ydSVg Measure retrieval and generation quality instead of guessing whether your RAG application is actually working well. 8. LangGraph — build agents that remember their workflow state https://t.co/YUoJouFV4U Create stateful, multi-step agent workflows with persistence, human-in-the-loop controls and more predictable execution. 9. Dify — build AI applications visually https://t.co/jx5BZ1V51R Combine workflows, models, knowledge bases and tools to build AI applications without wiring every component from scratch. 10. Letta — build agents with persistent context https://t.co/kPHZ01oTTo Explore stateful agents designed to manage memory and maintain context beyond a single interaction. AI builders are moving beyond prompts and model demos. The real challenge is building the systems around the intelligence. All open source. Worth a bookmark.
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@Vinay_bharambe ·
The AI Engineer Roadmap Most People Get Wrong 📘📚 Everyone wants to jump directly into: - Claude - GPT-5 - AI Agents - MCP - LangChain - RAG - Multi-Agent Systems But here’s the uncomfortable truth: Most AI applications don’t fail because of LLMs. They fail because engineers skip the fundamentals. Over the last few years, we’ve seen an explosion of AI frameworks, but the underlying engineering principles haven’t changed. A great AI engineer is still a great software engineer first. Here’s how I think about the journey. 1. Programming Fundamentals Before touching AI, learn to write clean, maintainable software. Focus on: • Python • Data Structures & Algorithms • OOP • Git • SQL If your code isn’t scalable, your AI system won’t be either. 2. Mathematics Many engineers avoid math. That’s a mistake. You don’t need a PhD, but you should understand: • Linear Algebra • Probability • Statistics • Optimization • Basic Calculus These concepts explain why models behave the way they do. 3. Data Engineering & Analysis An AI model is only as good as its data. Spend time learning: • NumPy • Pandas • Data Cleaning • Feature Engineering • EDA • Visualization Real-world AI is often 80% data work and 20% modeling. 4. Machine Learning Don’t memorize algorithms. Understand when and why to use them. Learn: • Regression • Decision Trees • Random Forests • SVM • Clustering • PCA • Model Evaluation • Bias-Variance Tradeoff • Hyperparameter Tuning Model selection is an engineering decision, not a memorization exercise. 5. Deep Learning Neural networks aren’t magic. Understand: • Forward Pass • Backpropagation • Gradient Descent • Activation Functions • CNNs • RNNs • Transformers Frameworks change. Fundamentals don’t. 6. Specialized Domains Now branch into: • Computer Vision • NLP • Speech • Time Series • Recommendation Systems Depth in one domain is often more valuable than shallow knowledge across many. 7. Generative AI This is where most people start. Ironically, it should come much later. Understand: • Transformers • Tokenization • Embeddings • Vector Search • RAG • Fine-Tuning • Prompt Engineering • AI Agents Without understanding embeddings or retrieval, many RAG systems become expensive keyword search engines. 8. AI Engineering This is where software engineering meets machine learning. Learn to build production systems using: • FastAPI • LangChain • LangGraph • LlamaIndex • OpenAI APIs • Gemini APIs • MCP • Model Context Management • Caching • Streaming • Observability Building demos is easy. Building reliable AI products is engineering. 9. MLOps A model that isn’t deployed creates zero business value. Learn: • Docker • Kubernetes • CI/CD • MLflow • Monitoring • Drift Detection • Cloud Deployment • GPU Infrastructure #AI #AIEngineer #MachineLearning #DeepLearning #GenerativeAI #LLM #MLOps #Python #DataScience #SoftwareEngineering #SystemDesign #ArtificialIntelligence #TechCareers #Engineering
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@businessbarista ·
AI truths people hate to admit: 1) AI “not working” in your job is almost always a skill issue now 2) Transforming a shitty business with AI is like putting rocket boosters on a PT Cruiser 3) 90% of AI transformation has nothing to do with AI. It comes down to people, process, data. 4) Strict AI governance for enterprises is important. It’s also going to lead to a brain drain of top performers quitting to be closer to the frontier. 5) Job seniority & AI expertise are often inversely correlated now 6) CEOs are lying to employees faces if they say that AI won’t cause job loss long term 7) The least sexy AI initiative is likely the right one for most companies: get an enterprise LLM subscription and enable your entire org on the tools, starting with engineering 8) 90% of your time working with LLMs should be organizing and feeding the right context to a model 9) Most execs do not have a compelling answer to “what is your moat?” in a post-AI world 10) Most companies don’t have cohesive AI strategies. They have a hodgepodge of initiatives that don’t work together, have no measurable ROI, and don’t address the core issue.
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@_jaydeepkarale ·
Great AI engineers understand what's happening under the hood • Probability → LLMs • Tokens → Context Windows • Embeddings → Vector Databases • Semantic Search → RAG • Prompting → AI Agents • Tool Calling → Multi-Agent Systems • Attention → Transformers • Fine-tuning → Model Alignment • Evaluation → Benchmarks • Guardrails → Production AI • Context Engineering → Agent Memory • Cost & Latency → AI Architecture The fastest way to build AI systems isn't chasing every new framework. It's understanding the layers they're built on.
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@kylegawley ·
none of my AI generated code works but I post about how great AI coding is on X because I don't want to look like I'm being left behind
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@karrisaarinen ·
One unproductive AI discourse pattern keeps to be how individual workflows preferences are talked as the universal hallmark of software engineering. Group 1: A solo builder with agents, their preferred stack, and a pile of markdown files, working on their own apps, is the right way to build and everyone else ngmi Group 2: A much larger group building with agents at scale inside companies, where coordination, reliability, shared systems, and organizational complexity create a very different set of problems which most people don't hear about. tbh individual workflows can still be directionally useful to show new ideas, but they can also be not stable, and enterprises might have very different problems that individuals don't ever have. It's like why small startups don't need to or shouldn't operate like Google, but Google kind of has to operate more or less like company of Google's scale. All ideas are good but much of the AI narrative still very confidently comes from group 1 too little from group 2 (with few notable exceptions).
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@xCryptoAlucard ·
While reading more about AI models, i realized most of what we use today are LLMs. Large Language Models. At their core, LLMs are really good at one thing, predicting the next piece of text. that’s why they’re great at generating👇🏼 text code ideas explanations but they still live inside the chat box they don’t actually use software the way we do. if you want something done, you still have to go and do it yourself open the website, navigate the interface, click the buttons. That’s where the idea of LAMs (Large Action Models) starts to get interesting. instead of predicting the next word in a sentence, these models learn sequences of actions. things like👇🏼 opening a website navigating menus clicking buttons filling forms basically interacting with software the same way a human would. @ActionModelAI is exploring this idea training models to learn real workflows from human actions. Still early but the shift from predicting language to learning real workflows feels like a meaningful step for AI systems👾
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@jorgemanru ·
I’m as bullish on AI for programming as anyone, but when I compare what I see day-to-day in my own work using frontier LLM models with some of the futuristic claims out there, it often feels like there’s a bit too much storytelling going on. Maybe "replace your SaaS software with vibe-coded apps" will be possible someday, who knows? But if you claim you’ve already done it... I’m looking forward to the postmortem.
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@alex_prompter ·
Anthropic just dropped a paper on 400,000 Claude Code sessions, and the headline finding flips a year of assumptions: domain expertise, not coding skill, is what makes an AI agent succeed. 235,000 people. Seven months. October 2025 to April 2026. The division of labor in a typical session: → You make about 70% of the planning decisions (what to build) → The agent makes about 80% of the execution decisions (how to build it) The agent runs the keyboard and you, the judgment. The gap widens from there. The more you understand the domain, the more the model does per instruction. A novice prompt sets off around 5 actions and 600 words of output. An expert prompt sets off 12 actions and 3,200 words. Same model. Same subscription. Five times the output. The only variable is the person typing. The success numbers track the same line: → Expert-rated sessions reach verified success more than twice as often as novice ones → When a session goes wrong, novices abandon it 19% of the time. Everyone else, 5 to 7%. And a coding background barely matters. Lawyers, analysts, marketers, and managers all succeeded within a few points of software engineers. Managers landed at the top. The gains come from competence, not mastery. A working grasp of the problem captures most of the benefit. Deep specialization adds a little on top. The bottleneck was never the syntax. It was always how well you understand the thing you're trying to build. LLMs don't think, you do.
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@RoundtableSpace ·
13 OPEN-SOURCE REPOS FOR AI AGENTS THAT FEEL ILLEGAL: Browser Use: Lets AI agents control a browser and complete web tasks. https://t.co/u02WwDgTh5 OpenHands : AI coding agents that can edit code, browse, use terminals and work on repos. https://t.co/bGJNZXrjYF Agno: Build AI agents with memory, tools, knowledge and multi-agent teams. https://t.co/So3UeNyUrq smolagents: Hugging Face’s lightweight framework for agents that write and execute code. https://t.co/l4PZyrG3ZN Pydantic AI: Build reliable, type-safe agents with structured outputs and tools. https://t.co/2ZNavVVTeE CrewAI: Create teams of specialized AI agents that work together. https://t.co/uvgoOhhjcm LangGraph: Build stateful agents with memory and controllable multi-step workflows. https://t.co/mTmgIE2rK3 AutoGen: Microsoft’s framework for conversational and multi-agent AI systems. https://t.co/sgtmOpOeOB LlamaIndex: Connect AI agents to documents, databases and private knowledge. https://t.co/p8OakXt6V5 Mastra: TypeScript framework for building agents, workflows, tools and memory. https://t.co/ZXyOwEeV69 Letta: Build agents with persistent memory that carries across sessions. https://t.co/Z2FZKPvkTm Cline: Coding agent that edits files, runs commands and works directly inside your IDE. https://t.co/blgkyn0Xqt Aider: AI pair programmer that works with real Git repositories from your terminal. https://t.co/wyWQelcltN
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@0xlelouch_ ·
Had a discussion with a colleague about AI. AI isn’t giving you skills. It’s amplifying whatever you already have. 1. If you write messy code, you ship bugs faster. 2. If you understand the domain, you ship features faster. 3. If you know debugging, you find root cause in minutes, not days. In this age, “learn AI” is not the plan. Go deep on fundamentals, then use AI to scale your output. But go deep, that agency and will to understand the depth of things is not going anywhere!
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@itsolelehmann ·
6 hills i'll die on in the AI age: 1. a lot of people are using AI for morally questionable stuff and nobody's saying anything fake UGC, manufactured testimonials, AI slop comments. i think it's fraud. bad for society and i'll never support it. - 2. fundamentals > shiny new tools. the tools change constantly. new models, new features, new platforms every week. but if you know how to give clear instructions, build reusable skills, and think in systems, it works on anything. you might switch from opus to chatgpt tomorrow. the people who master the underlying principles are the ones winning regardless of what tools they're using - 3. marketing > engineering. the best product with no eyeballs loses to an okay product with 100k followers every single time everyone can build a product now, but not everyone can build an audience human attention will always be scarce - 4. AI moves fast (honestly i get confused sometimes), so i'm a firm believer in explaining things as simply as possible. most AI content loses people in the first 30 seconds because it assumes the reader already has some level of technical know-how if someone has never opened a terminal, that's my starting point. the best teaching makes complex things feel simple (feynman technique maxxing) - 5. automate the busy work so you can go outside. i got into AI because i wanted more time with my family, not less. the whole point of automation is freedom. if you're spending more hours at your desk than before you started using AI, something went wrong - 6. i'm an AI optimist. i think these tools give normal people superhuman leverage that used to require entire teams and will ultimately be net beneficial for society but it only matters if you use it to build a life you actually enjoy, and that's your choice
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@Layton_Gott ·
AI can be insanely powerful for software devs… But ONLY if you use it right. Here's how to use it: 1. Be stupidly specific. Never say "build me a login page." Say "React login, TypeScript, JWT auth, error boundaries, loading states, Tailwind." More detail upfront = less fixing later. 2. Never let AI make architecture decisions. AI writes great code. It has no idea what code should exist in the first place. You decide the structure. AI fills it in. 3. Test everything AI writes. Run it. Break it. Try edge cases. AI writes code that looks perfect but fails in production. The 5 minutes you spend testing saves 5 hours debugging later. 4. Make AI explain what it wrote before you ship it. If the explanation doesn't make sense, don't ship it. This one habit has saved me from more bugs than any testing framework. 5. Give AI full project context before asking for a feature. Without context it writes code that works alone but breaks everything around it. The developers who dominate the next few years won't write more code. They'll use AI better than everyone.
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@milan_milanovic ·
AI can write code, but it can’t replace independent judgment As a software engineer, you need enough depth to think on your own. Without it, you fall into cargo-cult programming or chase trends you don’t understand This was true before AI. It’s critical now The real divide isn’t AI vs humans. It’s engineers who can spot errors, gaps, and hallucinations vs those who can’t Tools produce answers. Knowledge evaluates them If you can’t tell when AI is wrong, speed turns into risk The edge comes from learning without shortcuts: 1. Read good books 2. Take courses that explain fundamentals 3. Join workshops 4. Build small prototypes 5. Test ideas in code Use AI daily, but build your own understanding first, or you’ll copy your way into other people’s mistakes
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@chadwahl ·
“How do I adapt in this world of AI?” I get this multiple times a day from all ranges of people. I see the transformers are the people who ask the “dumb questions,” try stuff that fails, build things that end up interesting but not impactful, and put themselves out there without knowing what they’re doing. They’re building their AI intuition muscle. This AI intuition is fundamentally changing how they think about the world, how they approach problems and opportunities because the calculus for the cost of just doing things has changed. No longer writing RICEFs, KDDs, requirements docs, and long drawn out plans about plans. Scrum is dead, waterfall is dead, there is only building. When I can build so fast that working code is my requirements, it changes your business process to operate in the mode of continuous integration and continuous deployment (CI/CD). The idea of testing and validation is flipped on its head. You have a crystal ball looking into your own future right now, then run simulations about how this new future might benefit or hurt your business before you commit it to main. Then you parallelize complex efforts on top of this by having the right security and scaffolding, and this unleashes a whole new level of human-computer symbiosis allowing people to collaborate in powerful new ways. This is not only transforming the technical building but the compounding cycles of learning that you get in a 10-50x faster OODA loop. It is going to unleash a whole new level of human intelligence, new ambition, new possibilities. I’ve never been more bullish about our future of human and artificial intelligence.
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@johncrickett ·
Tips for AI-Assisted software development: Tip: Use AI to Challenge Ideas and Assumptions Many developers use AI the way they have used Google or Stack Overflow, to get answers. A better way is to use it to test your thinking. Often we choose a database because we’ve used it before. We design an API a certain way because it feels right. We skip a cache layer because scale probably won’t be a problem yet. Each one is a bet. And too often, those bets go unchallenged, even by us. We’re following a pattern, not consciously making a choice. AI can help us do better by acting as a sounding board. Not to make the decision for us, but to pressure-test the reasoning behind it. Ask it to find flaws. Ask it to argue against our approach. Ask it what could break, scale poorly, age badly, or fail in production. The value isn’t that AI is always right. It’s that it surfaces issues we haven’t considered. Do this in practice: - Before asking “how do I do X?”, ask “what could go wrong if I do X this way?” - Use a devil’s advocate prompt: “Play devil’s advocate. Give me the strongest case against this decision.” - Challenge architecture before you build it: “What are the three weakest points in this design?” - Run a pre-mortem: “Assume this feature fails in production six months from now. What are the most likely reasons?” - Re-examine your defaults: “Make the case against the framework, pattern, or tool I usually reach for.” We should use AI like a sparring partner to sharpen our thinking, not just as a search engine.
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@syedbalkhi ·
My X algorithm makes it look like AI is taking over everything, the reality is far from it. If you want to see it for yourself, ask an avg. small business owner how they're using AI in their business. Or ask a group of college students, how they're using AI. You'll quickly realize how SLOW the adoption really is. Here are some of my findings: - Most people use AI as a glorified Google search. This is the Primary use-case in my finding. - Others are using it for generative content (i.e emails / etc). This sounds cool until you watch the workflow. It's quite manual and requires MANY iterations to get a single text output. There's no agentic AI do it for me stuff happening. Most folks are NOT using Claude Code or Cowork. - Now let's talk about vibe coding. At this point, I have spoken to over a few dozen SMBs who attempted to "make their own apps" to replace SaaS tooling. On surface the "popular" vibe coding platforms made it look very nice. Until the endless debugging started. One SMB owner showed me that they spent $3k+ on a vibe coding platform and countless hours building an internal app to replace a SaaS but failed ... because the vibe coding platform was making up numbers and putting placeholders that looked real 🤦♂️ Basically the doom and gloom scenario that some folks on X share about AI taking over everything is way overrated. The adoption is very slow in real world. Now don't get me wrong, I love AI and I think it's only getting better. But like most major technologies, the adoption and penetration will take a long time. Any smart "AI-first" company will embrace Human first and Human last in all workflows. ^^ We have a lot of AI implemented in @awesomemotive but you have to monitor things. If you're just getting started, don't think you're too late. You're very very early! If you're a business owner, dive in and learn AI. But don't rush things. Be smart about it. Aside from tech debt, we'll soon discover and experience ... prompt / AI skills debt as well. Fun times ahead.
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@syedbalkhi ·
I went to an "AI workshop" event with CEOs / Owners of companies all very successful in their own industries. But not everyone is in tech or the folks that hang out here on X. Majority were blown away that they could build Custom GPTs!! Maybe 8 out of 70 people or so were doing interesting stuff with AI. Rest were using it as a glorified search platform. Sometimes this perspective helps. There is a long road ahead. Adoption of Agentic AI feels fast here on X but is actually quite slow in the "real world". The feeling reminds me a lot of WordPress in 2006. Truly exciting times ahead.
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@jspeiser ·
Forget the AI influencers. Here's what founders are actually doing with AI right now, straight from Hampton's private slack: What they're building: AI SDRs for sales outreach. AI BDRs handling pre-qualification + warm transfers. Financial models reviewed by AI "VC panels." Full 50-page client reports generated in 30 seconds. Websites + VSLs built in under an hour using Lovable. Tools getting the most buzz: Lovable, Claude, Cursor, Windsurf, Grok 3, Perplexity Deep Research, n8n, Gumloop, LittleBird, MCP servers, PydanticAI, Manus The real conversations: "Claude was tougher on my investment memo, but it hallucinated, so I had to tell it to stop making stuff up." Devs are resistant to AI beyond autocomplete. Founders are frustrated. Enterprises want AI but don't trust it at scale yet. Knowing stuff is no longer a competitive advantage. The opportunity everyone sees: Managed AI support services. AI-powered ops experts. Tools that actually integrate (not just look good in demos). The pace of this channel, and ai: Unreal. The takeaway that causes me the most anxiety; Founders who aren't experimenting are already behind.
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@RaulJuncoV ·
AI didn't flatten the playing field. It split it into three tracks, and most engineers don't know which one they're on. Three years ago, "senior developer" meant writing good code fast. AI cleared that bar for anyone who shows up and uses it. Here's what's left, and it's not code: 1. Orchestrators They design the flow, own the failure modes, decide what runs and what happens when it breaks. They don't write every piece. They decide the architecture around the pieces. This group has been thriving for two years while everyone else panicked. 2. Systems builders AI generates code fast. It has no idea what write contention does to your database at 10x load, or why that missing index takes down prod in six months. More code shipping means more blast radius. Someone has to own that. These people got more valuable, not less. 3. Domain translators AI closed the gap between idea and implementation. It can't tell you which idea is worth building. The engineers who can are usually quiet, easy to overlook, and the first ones you notice when they leave. If you're struggling right now, it's not because AI is coming for you. It's because you never picked a track. Thoughts?
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@basedbrickpush1 ·
Everyone is dead wrong about AI. People could be using it to do great things and have massive leaps in productivity in almost any job. But instead they're getting it **completely** wrong. Think about it - whenever there is some hype about AI it is ALWAYS about how 'human' it sounds and how good it is starting to get at things that humans can do. Things like: AI art AI music AI approaching consciousness, talking philosophy, etc etc. Ever seen the movie "Good Will Hunting"? If you haven't, you should check it out. Great movie, great dialogue, great writing overall. Matt Damon plays this genius kid. He's in his early twenties and when confronted with authority he enjoys outsmarting them. At one point he talks to a shrink (Robin Williams). Does his usual thing. Then Williams tells him: "Kid, if I asked you about art, you'd tell me all about van Gogh and Monet and Michelangelo. You'd give me their Wikipedia history. Recite all sorts of facts. Like a book report." "But you can't tell me what it smells like in the Sistine Chapel. You can't tell me what it's like to look up at that ceiling in wonder and be amazed at the spectacle unfolding right in front of your eyes." Beautiful scene. Shows us that the experience of being human is much more than just being able to blindly regurgitate facts. And yet, it seems like everyone is focused on making AI do human things. But it's not a human. It will never be a human. The amount of complexity necessary to even remotely get close to a human brain would slurp up all the datacenter capacity on the planet... and you'd still not even get close. (and I'm not even talking about a particularly remarkable brain here. I'm talking about a brain like uncle Bob who gets too wasted during Christmas dinner and starts yelling about politics? Even that level of brain is impossible to replicate) AI is a machine. It does machine things incredibly well. We've automated quite a few SOPs (Standard Operating Procedures) in our agency. AI makes us more productive because it does computer stuff better than the previous generation of computers. So if you're wondering on how you can use AI? Think more in that direction. Use it as a stronger, better, smarter, more efficient computer. Don't make it do human things. It is shit at human things. Because it is not a human. And never will be. Talk soon, Arno P.S. I am always keeping an eye on whatever new is brewing in the world of AI, obviously. Part of the job. We're ranking our clients in both Google AND the big LLMs. So I'm not anti-AI. I just see it for what it is. A big powerful machine. But still a machine. P.P.S. If you'd like to know more about how I work with clients - I've put together this video: https://t.co/28QFoSqK18
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@robj3d3 ·
In light of my recent updates to my AI tweet generation tool, I've gotten this message over and over again: "If this progresses and 100% AI written tweets become widespread then what’s the point? Would we not just be watching robots talk to each other?" Here's my response: tl;dr AI is a tool not a replacement for personality. AI posts and replies are different. AI posts can be good conversation triggers. AI replies are bad karma. "Fair question and this is the #1 point of backlash I get. My view on it is that AI is a tool, not a replacement for your personality. Social media and personal branding is not personal or social if you replace it with AI bots. We're already at the point where you can generate AI content and post it, everyone has ChatGPT. Anyone who uses these AI generations as 100% of their brand will get nowhere and rightly so. That said, as a tool to leverage what you are already doing, what you are already building, it's great. It gives you inspiration to expand from, it helps you articulate your thoughts and ideas into engaging content so it reaches more people. That's what I'm trying to build. There will always be people who will use it in a 0-effort way but those people won't get anywhere because it's not built for that and for quite a while longer I think people will see through it - it will lack substance. AI reply tools I am against because they automate conversation, which is stupid. That's what you're getting at I think with "robots talking to each other". At the end of the day, the X algo decides what to show to people and what not. And the X algo like any other social media algo is designed to propagate what people like to consume. Then gets into the argument of are we the problem, is the X algo the problem, or is the AI content generation tool the problem? If AI helps you to write genuinely insightful engaging ideas that helps you and others to expand your horizon or trigger conversation, is that a bad thing? If it's creating slop humans don't want to consume, it shouldn't be pushed by the algo. If it is being pushed, there's a problem with the algo. Again, posts are one thing and replies are another. Posts are the source of conversation and imo AI can be leveraged to expedite the crafting of engaging content - by far there's a much higher demand for viral engaging content than there is a supply (which is why everyone sees the same viral things over and over again). AI slop posts won't get pushed (assuming algo does its job) and isn't something we should worry about. AI replies that we can't avoid and fake human interaction are different and I stand against that."
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@alvinfoo ·
Everyone is rushing to build AI agents. Very few are asking the right question: Should this even be an agent? On one side: Automation → reliable, predictable, scalable On the other: AI Agents → flexible, powerful… but costly and sometimes unpredictable And yet, today? Startups are lining up for agents, even for the simplest tasks. Here’s the reality: Not everything needs intelligence. Some problems just need good systems. If a workflow is: • Repetitive • Rule-based • Clearly defined Automation will outperform AI every single time. AI Agents shine when: • The problem is ambiguous • Decisions require context • Tasks can’t be hardcoded That’s where intelligence creates leverage. The winners in this wave won’t be the ones who use the most AI. They’ll be the ones who know when NOT to use it. Don’t follow hype. Design with intention. Because in the end: Automation builds efficiency. AI builds capability. Knowing the difference builds great companies.
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@TheGeorgePu ·
I don't just build AI. I run my own agents all day. They schedule my posts, pull my research, watch my numbers. What the threads won't tell you: they break in boring ways, and I babysit them more than I'd admit. Useful. Not magic. Both true.
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@elvissun ·
tech twitter has been having the same conversation for two years: what agents can do. I live in this bubble and build agents for 12hrs/day. but this bubble breaks the moment your agents hit the real world: earlier this year I posted on reddit about using claude code as your PR operator in r/publicRelations 32k views. 32 upvotes. 191 shares (an 1:6 upvote to share ratio) the gap is the story. some agreed with the post. most forwarded it. some excited, some horrified. the gap is the story. some agreed with the post. most forwarded it. some excited, some horrified. this is what AI adoption looks like outside the bubble. people are hesitant. reluctant. resistant. and underneath all of it, afraid. building capability is easy. gaining adoption is hard. the agents that win the next decade won't be the ones that can do the most. they'll be the ones the real world is willing to let in. build for that.
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@NotionCoach ·
There’s more AI content than anyone can keep up with. I keep coming back to conversations that show the work: agents, context, workflows, evaluation, and what changes when a tool becomes part of the job. My favorite conversations go deep on workflows like setting up agents, where workflows break, or how to think about context. The useful stuff is rarely the hottest take about the latest model. These are 8 podcasts I keep coming back to: How I AI (from @clairevo) Interviews and walkthroughs from people using AI in their actual work. Favorite recent episode is with an OpenAI engineer dives deep into mastering voice mode in ChatGPT Work & Codex. @every / @danshipper The Every team treats AI as part of a working media and software company, then writes about what holds up. I saved tons of notes from their Codex-for-knowledge-work material. Lenny’s Podcast (from @lennysan) Product and company-building conversations with people close to the changes. The best of the best go on Lenny's podcast. @latentspacepod / @swyx A strong bridge between the frontier-model conversation and the operator question: what can a knowledge worker or software team delegate now? @aiDotEngineer Short talks and full workshops from the people building production systems. Good for diving into evals, agent design, and hearing where good ideas fail in practice. @petergyang Super clear, practical walkthroughs of tools like Claude, Codex, and OpenClaw. Also doing more interviews – great, calm discussions that go into detail without getting overwhelming. @ycombinator Proven founders and YC legends, sharing more of a business strategy/zoomed-out perspective on how to win in an AI-driven market. @AlexFinn Alex covers every new meaningful model or feature update. Does he get excited about hardware? Yes. Are the walkthroughs still super helpful? 100%. If you are trying to build a practical AI habit, pick one of these based on the work that's top of mind. Did I miss any? Let me know below! What would you add?
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@sharyph_ ·
Anthropic built a new AI model called Mythos. It was so capable at finding security vulnerabilities…it found a 17-year-old bug in FreeBSD on its own…that they refused to release it publicly. It's restricted to about 50 enterprise partners. Most people are reading this as a scary AI story. Read it as a signal about where AI fluency is heading. The creators who are casually "using AI" for content generation are going to plateau. The ones building structured, intentional human+AI workflows…where human judgment stays in the loop…are going to widen their advantage. AI capability is accelerating. Which means the question is no longer "are you using AI?" It's "how responsibly and skillfully are you using it?" Responsible AI fluency means understanding what the tool can do, where it needs human correction, and what should never be fully automated. That's the differentiation…not which AI tool you use. How you use it.
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