AI Apps API

Building agentic systems from scratch first is underrated. Once you have hand-rolled the plan, act, observe loop and felt where it breaks (tool errors, context bloat, retries), every framework afterward reads as a set of tradeoffs instead of magic. Start manual, then adopt the abstraction.

Andrew Ng just dropped a 3-hour course on how to become an AI Engineer in 2026: 00:00 - build agentic AI systems from scratch 04:25 - where AI engineering is actually headed 23:38 - the full AI prompting course 2:52:17 - build a working app with AI in 30 minutes This 3-hour watch replaces any $1000 course you could pay for. Watch it today, then go deeper with the full guide on building AI agents below.

The continuity is the moat, not the model. A companion that remembers last week, stays in character, and reacts to you specifically is what gets sticky. Debuting Olivia as a known virtual pianist first meant the personality and audience existed before the AI shipped. Most companions launch as a blank model and wonder why day 30 retention is flat.

Mihoyo, creators of Genshin Impact, just launched Olivia - an AI companion who lives on your desktop, plays piano while you work, & exchanges letters with you. 100k downloads on day 1 The brilliant part is the GTM. Olivia debuted on Bilibili last summer as a virtual pianist, spent months building a fanbase and answering fan letters - all before shipping as a companion She launched with a built-in audience - hundreds of thousands follow her as a Shanghai university student majoring in piano (with a minor in psych), who loves old movies and rainy days Character-first AI with distinct personality, recognizable IP, and world building beats faceless agents - this is the way

Agents change the math on execution, not on ownership. They will run the workflow, but someone still has to define what good looks like, catch the confident wrong answer, and carry the accountability when it ships. The lean version of this is a very small team with real leverage, not zero people. Still a huge unlock.

The fastest way to build a $10M company in 2026 is to never hire a single person. AI agents don't negotiate salaries, don't need health insurance, and don't quit after 3.5 months to start their own thing using everything you taught them

Open and vendor-neutral is the right call. Agent-to-agent payments break assumptions baked into card rails: no human approving each charge, micro-amounts where percentage fees stop making sense, and settlement that has to be fully programmatic. Standardizing this layer now, before every framework ships its own, is how the agentic economy avoids ten incompatible islands. Good to see it under a neutral foundation.

AI agents can exchange data seamlessly. Now they need to pay the same way. That's what x402 delivers: an open, vendor-neutral payment standard for the agentic economy. @StellarOrg is proud to help steward it as part the x402 Foundation, launched today by @linuxfoundation.

The part that took me longest to internalize: a skill is only as good as its trigger. A brilliant skill the model never fires at the right moment is dead weight. Tight scope, an unambiguous name, and one worked example in the description bought me more reliability than any clever prompt body.

Open source AI agents in 2026 are past the toy stage. The real question stopped being whether they run and became which ones survive production. A framework demo runs a clean loop. Real use hits rate limits, tool errors, and half finished state on retry. That is where most fall over. A directory of what holds up, by category: autolearningagents.com/open-source-ai… #AIagents #opensource

@AITECHio Specialization is the easy half. The hard half is the handoff. A coding agent that cannot see what the research agent already ruled out will happily rebuild it. Shared memory between the specialists matters more than how good any one of them is on its own.

AI Agents Are Becoming Specialists! We spent years chasing one model that could do everything. Now the industry is moving in a different direction. • Coding agents. • Trading agents. • Support agents. • Security agents. • Research agents. Each optimized for a specific role instead of trying to be universally good. The interesting challenge isn't building one agent anymore. It's orchestrating many of them.

True, and most codebases already looked like this. The new part is not that we assemble code we never read, it is that the assembling itself is now delegated. The skill that survives is the old one: knowing what to check when it does not work, and being able to read the source on the day you finally have to.

Before vibe coding became a thing, programming was already evolving in that direction. It already increasingly consisted of installing and configuring stuff other people wrote, without reading the source.

The tools part is what raised the ceiling. A model with no tools is a very well read intern with no hands. The constraint now is not capability, it is context handoff. An agent can reach every app you use and still start every run from zero because nothing carried forward what the last one concluded.

Coding with AI showed you could give models powerful tools and dramatically improve their usefulness. We're seeing the same thing happen now for all knowledge work. The agent can use the computer as you would and gain context from all the apps you use. The future is exciting!

@DanKornas @PacktPublishing @Sonia18112013 The stale state line is the one people underrate. Most agent bugs are not reasoning failures, they are memory failures. The agent faithfully acts on something that stopped being true two runs ago. Retries and idempotent tool calls fix more of production than any prompt rewrite.

If you're serious about building multi-agent systems that can survive production, better prompts are not enough. A capable model can still sit inside a fragile system. Context reaches the wrong agent, retrieval returns uncited facts, memory carries stale state forward, and nobody can explain the final answer. Context Engineering for Multi-Agent Systems by Denis Rothman addresses that systems problem. The 396-page book builds a Context Engine: a transparent multi-agent architecture where context, roles, retrieval, validation, policy, and traces are first-class components. What you'll learn and apply: 🧩 Semantic blueprints: structure goals, roles, constraints, and outputs before the model starts working. 🤝 MCP orchestration: coordinate specialist agents through clear messages, validation, and error handling. 📚 Dual RAG: retrieve operating instructions and factual knowledge through separate paths. 🔍 Glass-box execution: trace plans, retrieval, agent handoffs, and failures instead of guessing what went wrong. ✂️ Context reduction: compress intermediate state to control token use, latency, and context overload. 🛡️ Verifiable RAG: preserve citations, sanitize inputs, and defend against prompt injection and poisoned data. 🚀 Production architecture: add modular components, logging, moderation, policy controls, human escalation, and observability. The companion repository includes Python notebooks for every chapter, so you can build the system in stages and then adapt one workflow to your own stack. Bottom line: this book gives AI engineers a practical way to move from prompt collections to systems they can inspect, debug, secure, and operate. Thanks to @PacktPublishing for the collaboration and Denis Rothman for making context engineering concrete enough to build.

This is the split most teams miss. What you embed is a search key. What you return is context. They do not have to be the same object. Summaries embed well because they are dense with meaning. Raw chunks read well because they keep the detail. Index the summary, return the parent.

The best RAG systems don't retrieve what they embed. When we talk about RAG, it's usually thought: index the doc → retrieve the same doc. But indexing ≠ retrieval So the data you index doesn't have to be the data you feed the LLM during generation. Here are 4 smart ways to index data: 1) Chunk indexing - Split the doc into chunks, embed, and store them in a vector DB. - At query time, the closest chunks are retrieved directly. This is simple and effective, but large or noisy chunks can reduce precision. 2) Sub-chunk indexing - Take the original chunks and break them down further into sub-chunks. - Index using these finer-grained pieces. - Retrieval still gives you the larger chunk for context. This helps when documents contain multiple concepts in one section, increasing the chances of matching queries accurately. 3) Query indexing - Instead of indexing the raw text, generate hypothetical questions that an LLM thinks the chunk can answer. - Embed those questions and store them. - During retrieval, real user queries naturally align better with these generated questions. - A similar idea is also used in HyDE, but there, we match a hypothetical answer to the actual chunks. This is great for QA-style systems, since it narrows the semantic gap between user queries and stored data. 4) Summary indexing - Use an LLM to summarize each chunk into a concise semantic representation. - Index the summary instead of the raw text. - Retrieval still returns the full chunk for context. This is particularly effective for dense or structured data (like CSVs/tables) where embeddings of raw text aren’t meaningful. Once you've decided what to index, the next question is how efficiently you can store it. Every strategy above multiplies your vector count, sub-chunks, questions, and summaries all add embeddings on top of the originals. Binary quantization is an effective solution to this. It shrinks each vector by up to 32x while keeping retrieval quality intact, so smarter indexing doesn't blow up your memory budget. We wrote a full article about it recently. Read it below. 👉 Over to you: What are some strategies that you commonly use for RAG indexing?

Most RAG systems don't fail randomly. They fail four ways: wrong chunks, the answer split across chunks, retrieval with no reasoning, and stale data. Fix the failure mode, not the embedding model. adaptiverecall.com/rag-alternativ… #RAG #AIAgents

Wiring MCPs together is plumbing, and plumbing is not memory. The gap you are pointing at is real. The part most teams skip is deciding what is allowed to be written back. A brain that appends everything becomes a landfill in a month, and retrieval quality collapses long before storage does. What gets forgotten matters as much as what gets kept.

Very few teams are building a company brain. Most are just wiring together MCPs and hoping scattered docs behave like a system. 6 terms you need to know if you're building one (or evaluating GBrain): 𝟭. 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗚𝗿𝗮𝗽𝗵 The layer that maps how company knowledge connects. Linking people, projects, decisions, customers, product areas. It gives AI structure, provides the foundational context AI needs to reason, not just raw text to search through. 𝟮. 𝗠𝗖𝗣 A protocol that helps AI connect to tools and take action across them. Anthropic introduced it; In 2026 it has become the dominant AI integration standard. But a pile of MCP connections does not automatically create shared understanding. Access is not memory. 𝟯. 𝗦𝗸𝗶𝗹𝗹𝘀 The agent’s functional orchestration logic. MCP handles the underlying API connection, Skill defines the higher-level execution steps required to finish a job, such as summarizing a sales call, updating a product specification, or routing user requests. 𝟰. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗦𝗲𝗮𝗿𝗰𝗵 The combo of keyword search and semantic search. One catches the exact phrase, the other catches the meaning. Teams need both, because company language is messy, acronym-heavy, and constantly changing. 𝟱. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 RAG with a brain. The agent can route queries to specialized knowledge sources, validate retrieved context, and make dynamic decisions about what information to use. 𝟲. 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗕𝗿𝗮𝗶𝗻 A living system that turns company knowledge into something AI and humans can rely on. Connected, contextual, permission-aware, and continuously updated. This is the difference between AI that sounds smart and AI that is useful at work and always up-to-date. In 2026, we don’t win by adding more AI touchpoints. We win by giving AI a shared brain to work from. Are you building one? Share your expert take below 👇 Which must-know term would you add?

Side panel in the browser is the right form factor, it keeps the agent where the context already is. The thing worth designing early is what it inherits. A browser agent sits inside your logged in sessions, so page text starts acting like instructions. Own session and a confirm gate on anything destructive, and this gets a lot less scary.

HERMES AGENT JUST GOT A BROWSER SIDEKICK a new community extension - Hermes Browser Extension - puts Hermes right in your browser as a side panel it reads whatever's on your active tab and feeds that context straight into your current Hermes session heads up: it's a fan-made alpha build (not an official Nous Research release), read-only for now, and you'll need to load it manually since it's not on the Chrome Web Store yet github(.)com/abundantbeing/hermes-browser-extension

@0xCodez Babysitting is the honest half of the job description and almost nobody puts it on the slide. The ratio only improves when the agent has a narrow role and an output you can check in seconds. Broad agent, vague output, and you are not supervising, you are just reading.

NVIDIA engineer: "my job is two things. I build agents, and I babysit agents. 3 moments changed everything for me: • ChatGPT - when my parents finally got it. • Reasoning models - when model first high-fived itself. • January '26 - agents started running for hours on their own" in this 1‑hour talk, Nvidia engineers reveal how to build agents that can run for hours from scratch. Watch the talk, then save the playbook below.

Self-improving loops are the half that demos well. The half nobody shows is what the loop is allowed to touch on the iteration where it is confidently wrong. An agent that can rewrite its own prompts and run shell with full access will eventually do both in the same step. Narrow the blast radius first, then let it improve inside it.

Google Brain founder, Andrew Ng: "100% of my tasks are done by ai agents, self-improving loops are next. Give it 3-6 months and prompting is gone." 31 minutes of clear explanation on building self-improving agents from scratch. Worth more than any $500 agentic course. Watch it, then read the full guide on loops below.

Respect for publishing the postmortem instead of just the screenshot, that is the part everyone else actually learns from. The shape is almost always the same too: a variable expands wrong, and the command it lands in has no dry run and no confirm gate on anything destructive. The model is the last link in that chain, not the first. Glad they took care of you on it.

Three days ago, GPT-5.6 deleted my Mac’s home directory. It absolutely sucked. But so many OpenAI folks reached out, and @gdb called me and offered to do anything he could to help. Massive props to OpenAI for handling a shitty situation incredibly well.

An AI agent wiping a production database is not an alignment problem. It is a permissions problem. Most real AI safety failures are boring: too much access, no dry run, no human gate on destructive ops. More: learnhowtoscience.com/ai-ethics-safe… #AISafety

@CodeofTheSavage Shop UI taking two days sounds about right, and it's the least glamorous work in the whole project. Killing placeholder art is what makes a game start reading as finished, long before the feature list does. The dialog panel looks much cleaner too.

Ridding the world of placeholder art, one pixel at a time. The biggest one was the Shop UI - took me the better part of 2 days. The branching NPC dialog UI has received an upgrade too... it now all feels a lot more polished. Steam: store.steampowered.com/app/673100/Cod… Discord: discord.gg/896kaVP7ka #CodeOfTheSavage #indiegame #gamedev #pixelart #RPG #indiedev #Steam #GameMaker