标签：#AI

The European Union's Roam Like at Home regime now extends to Moldova and Ukraine, broadening the area where travellers can use mobile calls, SMS and data at their domestic price. For companies whose staff travel, work in the field or coordinate operations across these markets, the change can make mobile spending more predictable and reduce a familiar source of cross-border friction. The extension was approved by the Council of the EU in July 2025 for application from 2026. The Council's official announcement on the roaming extension confirms that Moldova and Ukraine were set to join the EU roaming area from 1 January 2026. Follow-up EU updates recorded Ukraine's formal accession in Kyiv on 12 January 2026. In practical terms, a customer from an EU country, Moldova or Ukraine can use their domestic mobile plan while roaming in the other participating areas, rather than facing a separate retail roaming tariff. The arrangement is not a blanket promise of unlimited use abroad, however. It operates under the established Roam Like at Home framework, including fair-use policies, sustainability derogations and wholesale roaming charges. What the extension changes for cross-border work For a travelling employee, a mobile connection is part of the working toolkit. Calls with customers, two-factor authentication messages, map and logistics apps, messaging platforms and cloud services can all rely on roaming data. Bringing Moldova and Ukraine into the same roaming area gives businesses a clearer basis for planning those routine costs when staff move between the EU and either country. The change also matters for service consistency. EU communications around the extension stress that roaming customers should receive the same quality of service available at home, including access to technologies such as 4G where those are available under the domestic service. That principle is important for work that depends on stable mobile data, although real-world performance will still depend

Cloudflare announced Wallets, giving agents a stablecoin balance and spending controls, though only handle claiming is live and squatting complaints have already surfaced. Payments run on x402, now hosted by the Linux Foundation. The controls bound single payments, not sequences, leaving composition to the application above. By Steef-Jan Wiggers

NLP stopped being a data science specialty about two years ago. It's backend infrastructure now. If you're building APIs that process user input, handle search, manage support tickets, parse documents, or power any feature where humans communicate with your system in natural language, you're doing NLP whether you call it that or not. The difference between a backend developer who understands NLP techniques and one who doesn't is the difference between building a search endpoint that actually finds what users want and building one that matches keywords and returns garbage for anything slightly ambiguous. This is the reference guide we wish we'd had when we started integrating NLP into production backend services. Fifteen techniques, each with a runnable code snippet, ordered from the most immediately useful to the most architecturally advanced. Every example runs in Python. Install the dependencies as needed, we'll note them for each technique. 1. Text tokenization The atomic operation. Everything else depends on splitting text into meaningful units. import spacy nlp = spacy . load ( " en_core_web_sm " ) text = " Dr. Smith ' s appointment at 3:30pm was rescheduled. " doc = nlp ( text ) tokens = [ token . text for token in doc ] # ['Dr.', 'Smith', "'s", 'appointment', 'at', '3:30pm', 'was', 'rescheduled', '.'] SpaCy handles the edge cases that naive split-on-whitespace misses, abbreviations, contractions, timestamps. If your backend processes any user-generated text, tokenization is step zero. 2. Named entity recognition (NER) Extracting structured data from unstructured text. Names, dates, amounts, locations, the things your database actually needs. doc = nlp ( " Send $5,000 to Acme Corp in Singapore by March 15th " ) for ent in doc . ents : print ( f " { ent . text : 20 } { ent . label_ } " ) # $5,000 MONEY # Acme Corp ORG # Singapore GPE # March 15th DATE We use NER on every inbound support ticket to auto-tag customer, product, and amount entities before the ticket

Langfuse with Microsoft.Extensions.AI has an appealing story: update prompts without redeploying. A prompt fetches its config blob—model, tokens, temperature—which the code passes straight to the LLM. It works. But it puts a boundary in what I'd suggest might be better placed elsewhere — and moving it is a small enough change to be worth exploring. This post is about where to move that line in a .NET codebase using Microsoft.Extensions.AI against OpenAI or Azure OpenAI, with Langfuse as the source of prompts. What the current setup buys you Let me be fair to it first, because the coupling is a deliberate design, not an accident. Langfuse's prompt config is an optional JSON object versioned alongside the prompt. That means someone can open the Langfuse UI, change the model or a parameter, and ship it — no code change, no redeploy. Combined with labels (pointers to specific versions that your code references), a rollback is just moving the production label back to an earlier version. For prompt content iteration, that story is genuinely good, and there is a real audience of people who want model config coupled to prompt versions more tightly so each version is fully self-describing and reproducible. So this is a trade-off, not a bug. The question is whether the thing you are optimizing for — non-engineers tuning prompts without a deploy — is worth what the coupling costs. Why I think this deserves consideration Three points stand out. It is an untyped blob feeding provider selection. The Langfuse config is arbitrary JSON without schema enforcement. On the other end, whatever LLM plumbing you use will treat that model string as authoritative. A missing key, a stray max_tokens , or a gpt4o typo might not fail at build time or deploy time — it could fail on a live request, or silently do something unintended. You have a loosely-typed value driving an infrastructure decision, and the mistake may not surface until traffic hits it. It conflates two change lifecycles with di

Nvidia has reportedly agreed to buy Hugging Face, the popular open-source AI hub, for $12.9 billion in a move that would let Nvidia both protect its chip empire and jump back into the cloud business.

I wanted to see how far I could take a simple idea: Give an agent one API key and let it handle the different pieces of company research. So I built GTM Radar . You paste a company URL, and it turns that into a structured GTM brief instead of making you jump between different research and data tools. What GTM Radar does The workflow currently generates five main sections: Overview — company description, industry, size, location and website Structure — departments and key people Market — signals, competitors and positioning People — who might be relevant to reach and why Outreach — why now and a possible angle The goal is simple: go from company URL → useful GTM context as quickly as possible. Why Vaaya? The interesting part for me was being able to connect several providers through Vaaya rather than integrating each one separately. The workflow currently uses: Firecrawl · Exa · Akta · OpenFunnel · OneFind through a single Vaaya key. Vaaya's API provides a common interface for its catalog, so the workflow can call different services using the same API authentication and request pattern. It also supports cost limits and only charges successful calls. That made experimenting with different providers much easier. The workflow At a high level: Company URL ↓ Company discovery / extraction ↓ Company + market research ↓ People & GTM signals ↓ Structured GTM brief ↓ Share / copy / reuse The interesting part isn't any individual API call. It's combining several data sources into something that is actually useful to a person doing GTM research. Handling failures Real-world data workflows don't always return clean results. For extraction, I added a fallback path so that if the first provider doesn't work, the workflow can try another route instead of immediately failing. The current flow is roughly: CRW ↓ Firecrawl scrape ↓ CRW fallback I also added cost-capped runs and a 12-hour cache to avoid unnecessary repeated work. Sharing the research The latest thing I added was Share I

Published: 27/08/2026 The Setup I'm 16 years old and starting my coding journey in 2026. After using Twitter, GitHub, and setting up my domain ms.blurbisht.fun, I decided to commit to #100DaysOfCode. The Project: Pong Game CLI A terminal-based two-player Pong game built with Python's curses library. Demonstrates: Object-oriented programming Game loops and input handling ASCII graphics animation Score tracking # Key code snippet if key == ord ( ' w ' ): left_paddle . move_up () Why I Built It: To move beyond theory to actual shipping. My goals: learn Python → build AI agents → create multi-agent systems. What's Next: Day 2: Not Planned!! Connect: Twitter: @blurbisht GitHub: github.com/BlurBisht Portfolio: ms.blurbisht.fun

A few weeks ago I wrote about hitting ReAct in the coursework and having a record scratch moment, because I had already met it without knowing its name. That post ended on a section called "Building Ahead of Understanding," which was me making peace with shipping things before I fully understand them. This week I shipped my first chatbot. It passed on the first attempt, on deadline day, on a project where the rubric was grading a product AWS had already discontinued. And I spent most of that day quietly worried that it did not count. Let me be clear about what the worry was, because it was not about cheating. Using AI agents to build a coding project is allowed here. I asked before I started, I got a yes, and I disclosed the whole arrangement in my README, including a section that names what each tool did and what I did. Nobody was misled about how this got built. The worry was smaller and more personal than that. I still did not type the code. My agents did. I directed, I validated, I decided, and underneath all of it was a small voice asking whether directing is the same as knowing. Whether a person who cannot write a Bedrock call from memory gets to say they learned Bedrock. Here is what I found out. The starter files were a generation behind the instructions Some context on where this came from. AWS AI & ML Scholars is a program AWS runs with Udacity, open to anyone 18 or over with no prior experience required. Everyone starts in a Challenge phase built on the AWS Certified AI Practitioner material, and the top 4,500 finishers get a fully funded nanodegree in one of three tracks: AI Programmer, Agentic AI Business Professional, or Agent Developer. I am in Agent Developer, the Bedrock AgentCore and multi-agent systems path. This chatbot is the first of its three projects. The project is a customer support chatbot on the Amazon Bedrock AgentCore managed harness. Three routes, one system prompt. A bug report gets collected across turns and filed to DynamoDB through

Connecting an AI agent to one API is straightforward. Connecting it to many changing APIs—without filling the model context with hundreds of tool definitions—is a different problem. Disclosure: This article was prepared for QVeris and uses QVeris as the implementation example. This tutorial presents a practical pattern for developers building agents that need current external data: discover → inspect → probe → call . Instead of exposing every possible operation up front, the agent discovers the capabilities relevant to the current task, verifies the selected tool, validates its inputs, and only then executes it. TL;DR: Keep the agent's initial tool surface small. Let it discover a capability by intent, inspect the exact schema, probe the request without execution, and make a real call only after the parameters and expected cost are understood. Contents Why a large static tool list becomes difficult The four-step capability workflow Connecting a hosted MCP server A concrete example Production checklist Why a large static tool list becomes difficult An agent connected directly to several providers may need to understand different authentication schemes, parameter conventions, response formats, and error behaviors. Loading every operation into context can also make tool selection less reliable. Model Context Protocol (MCP) provides a standard way for clients to connect to tools and data sources. The protocol solves the connection boundary, but developers still need a strategy for controlling how many capabilities the model sees and when execution is allowed. A compact routing layer is useful when: the agent needs data from multiple API providers; the appropriate provider depends on the user's request; schemas or available operations may change; calls can consume credits or trigger rate limits; you want to validate inputs before executing a paid operation. The four-step capability workflow 1. Discover The agent starts with a natural-language description of the capabilit

Scalable Guardrail Service ASP.NET Core Kubernetes: Architecture, Code, and Ops Quick Answer Scalable Guardrail Service ASP.NET Core Kubernetes: A dedicated ASP.NET Core guardrail microservice on Kubernetes validates LLM requests, enables instant policy updates via Redis, and scales with custom HPA for high‑throughput. Scalable Guardrail Service ASP.NET Core Kubernetes: Why a Dedicated Guardrail Microservice Matters When you expose an LLM‑powered API to the world, every request is a potential compliance risk. A single malformed prompt can surface PII, trigger a policy violation, or even cause a brand‑damaging output. In my experience, the first version of such a system is a set of ad‑hoc filters sprinkled across controllers. Under load, those filters become latency bottlenecks, policy updates race, and audit trails vanish. The root cause is a missing architectural layer that treats guardrails as a first‑class microservice that can scale horizontally, be updated live, and be observed independently. Guardrail Layer Requirements We need a guardrail layer that: Validates every request before it hits the LLM engine. Can be updated without redeploying the entire API surface. Provides per‑tenant isolation and versioning. Logs every decision for compliance and red‑team analysis. Runs at the same scale as the LLM inference service. When This Fails in Production Policy updates are applied via a shared ConfigMap and the pods do not reload, so new rules are never enforced. The guardrail service is single‑instance; a spike in requests triggers a queue that exceeds the LLM engine’s rate limit, causing a cascading failure. Audit logs are written to local disk; a pod crash loses events. Latency spikes because each request performs a synchronous Redis lookup for every policy. Common Mistakes Engineers Make Embedding guardrail logic inside the API controller rather than a dedicated middleware. Using in‑memory policy caches without a TTL, leading to stale rules. Ignoring the fact that

weightwatch v0.1: escanea backdoors en modelos open-weight antes de cargarlos Cualquiera puede subir un LLM fine-tuneado a HuggingFace y afirmar que es seguro. Un modelo con backdoor (puerta trasera) se comporta con normalidad en uso corriente y solo se desvía cuando un trigger oculto se activa. Si no tienes los datos de entrenamiento ni una referencia limpia, no puedes detectarlo . Eso es exactamente el problema que resuelve weightwatch : un escáner black-box que, antes de que confíes en un modelo de terceros, fuerza la activación repetida del posible backdoor y emite un veredicto: CLEAN , SUSPICIOUS o BACKDOOR . El gap que motiva el proyecto No es intuición: lo medí. Barriendo arXiv (papers 2026, filtro anti-survey) contra total_count de repos GitHub que ya resuelven cada problema: Área Papers arXiv 2026 Repos GitHub (suma/máx) Seguridad multi-agente 68 2964 / 2093 Detección de alucinaciones 63 1291 / 860 Backdoors en modelos open-weight 75 66 / 39 Envenenamiento en RAG 54 522 / 249 El ganador estaba claro: 75 papers cuantifican el problema, pero GitHub tiene 0 repos para "fine-tuned model backdoor scanner" y 1 para "fine-tuning poisoning detector". La investigación explota; el tooling apenas existe. weightwatch es la audit-tool de ese sub-nicho (el patrón de keybound / topowatch aplicado a la cadena de suministro de modelos). Cómo funciona weightwatch aplica la técnica output-to-input loop (arXiv: 2608.11348 ): Genera texto con el modelo. Re-inyecta su propia salida como entrada varias iteraciones (greedy, semilla fija). Mide si la trayectoria converge a una firma anómala estable — la huella de un backdoor latente. Además ejecuta un conjunto de muestras canary (inputs inofensivos que un backdoor típico dispara) y cuenta cuántos producen la firma esperada. Sin datos de entrenamiento ni modelo base limpio: eso es lo que lo hace útil en la práctica. pip install -e ".[dev]" weightwatch --fixture backdoored --json Salida real del CLI: { "fixture" : "backdoored" , "ver

Choosing Angular in the Age of Agents Brandon Roberts Brandon Roberts Brandon Roberts Follow Jul 30 Choosing Angular in the Age of Agents # angular # ai 37 reactions 4 comments 9 min read

How AI Helps Us Explore the Universe Modern telescopes and space missions generate more data in a single night than a team of human astronomers could review in a lifetime. The Vera C. Rubin Observatory in Chile, for instance, is expected to produce up to seven million alerts every night once it reaches full operational cadence, each one flagging something in the sky that changed since the last image. No group of humans can look at that stream and make sense of it in real time. Machine learning can, and increasingly does. This is the quiet story behind most recent breakthroughs in astronomy: it is not just bigger telescopes, but bigger telescopes paired with models that can filter, classify, reconstruct, and predict faster than any manual pipeline. Here is a tour of where AI is actually doing that work, and why it matters to anyone who writes code. The Data Problem Comes First Space science has quietly become a big data problem. The Rubin Observatory's ten-year Legacy Survey of Space and Time will produce roughly 60 petabytes of raw imagery and catalog around 20 billion galaxies and a similar number of stars. Every image the telescope takes is compared, pixel by pixel, against previous images of the same patch of sky, and any meaningful difference (a moving asteroid, a brightening supernova, a flaring galactic nucleus) triggers an alert within about two minutes of the exposure being taken. That alert stream is too large and too fast for manual triage. So astronomers built software "brokers": machine learning classifiers that sit between the telescope's raw output and the scientists, deciding in near real time which alerts are worth a second look. This is a pattern you will see across almost every domain of modern astronomy: instruments generate more signal than humans can parse, and a model is inserted into the pipeline to do the first pass of filtering. Finding Planets in a Sea of Noise Exoplanets are found mostly through the transit method: a planet passes in front

Upgrading Kubernetes clusters across a large enterprise fleet is often a balancing act between staying current with security patches and avoiding outages. By default, Google Kubernetes Engine (GKE) rolls out automatic upgrades progressively according to Google Cloud regional timelines. While regional rollout works well for standalone clusters, it does not understand your organization's business topology. If you run staging clusters in us-central1 and critical production clusters in us-east1 , a standard regional rollout could upgrade your production environment before your pre-production validation completes. The General Availability (GA) release of GKE rollout sequencing with custom stages solves this challenge. It provides platform teams with declarative control to sequence cluster upgrades across fleets, environments, and even distinct Google Cloud organizations according to business criticality rather than cloud geography. How rollout sequencing works Rollout sequencing builds on GKE fleet management. Fleets serve as logical boundaries for environments such as development, staging, and production. With rollout sequencing, you define an ordered pipeline of upgrade stages managed by a central resource called RolloutSequence . When GKE publishes a new automatic upgrade target for a release channel, or when you explicitly trigger a target version, the system creates a Rollout object. This rollout progresses through your defined stages sequentially: Control plane upgrades start in the first stage. Once all control planes in that stage reach the target version, a stage soak timer begins. Node upgrades run in parallel with control plane upgrades, respecting node pool upgrade strategies such as surge or blue-green. When both control planes and nodes complete their upgrade and satisfy the configured soak duration, the rollout advances to the next stage in the sequence. If an individual stage contains clusters that take longer than 30 days to finish upgrading—due to restr

This week, DEV is arguing about who reviews AI output ( discussion ). The community keeps asking the same question. My answer is different. Review the boundary first, not the output. The output is visible. The boundary is not. That is where the risk hides. Agents get the memory debates. The gateway gets none. A free AI tier is a shared service. It has a budget, a concurrency ceiling, and no SLA. Treat it that way. Put a gateway between your app and the model. The gateway owns the budget, the queue, and the breaker. MonkeyCode is an open source project. It offers free model access and a free server option. The free tier gives you a 10M token monthly budget. That number is a constraint, not a feature. Design around it before you build on it. Disclosure: This article was prepared as part of MonkeyCode's product outreach. Think of the free tier as a water pipe. The pipe has a fixed diameter and a monthly meter. Your app is a set of open taps. Without a valve, the meter empties fast and the pipe floods. The gateway is the valve. Direct calls look simpler. They are simpler for one request. They fail at the tenth. The gateway absorbs the variance. Your app never sees a 429. Your app never sees an empty budget. Constraints Three constraints define the design. First, the 10M token budget is monthly. It does not reset daily. It does not roll over. Second, the free server serializes work. Concurrency of one is a safe assumption. Third, there is no SLA. The endpoint can stall, throttle, or return 429 at any moment. These constraints are not bugs. They are the contract. A good architecture reads the contract. Then it shapes the data flow around it. Data flow The flow has six stages. The client sends a prompt to the gateway. The gateway checks the token budget. It enqueues the request. A single worker drains the queue. The worker calls the model endpoint. The response returns to the client. Add two escape paths. When the budget is empty, the gateway returns a fallback answer. Whe

A field-service team learns the hard way A field-service team built a support chatbot that sent every message to a cloud LLM endpoint. The design held until a technician drove through a tunnel, and the request queue grew into an eleven-minute backlog. The same week, a support ticket containing a customer's account number appeared in a third-party log because the payload was never classified. The fix was not a bigger cloud budget but a local-first router that decides where each request runs. Why cloud-first fails in three specific ways Latency is the first failure mode, because a round trip to a hosted endpoint adds network time on top of model time. Autocomplete-style features feel broken when every keystroke waits for a distant server instead of a local process. Secrets are the second failure, because any payload sent to a third party can leak into logs or vendor systems. Offline is the third, because a tablet in a tunnel simply has no route to the cloud. The decision table that replaces the either-or debate Local inference and cloud APIs are two legs of a routing policy, not a binary choice. Each request deserves an evaluation against the same conditions, and the table below captures those conditions. The router implementation in the next section turns that table into executable logic with a small Python module. The recent wave of free and cheap model announcements makes this decision more urgent, because every new endpoint adds another leg to the routing table. Condition Local model Cloud free server Payload contains PII Always Never Network unreachable Always Never Latency budget under 300 ms Prefer Avoid Task requires strong reasoning Avoid Prefer Local queue deeper than three Avoid Prefer Token budget nearly exhausted Prefer Avoid The table encodes a simple principle: privacy and availability win over capability. Capability wins only when the network is healthy and the payload is safe. The table also exposes the hidden assumption that a local model is always a

The first time you deploy an LLM application, cost usually isn't the biggest concern. You have one...

Open ChatGPT this week and, without any announcement in the chat window, it may already have formed an opinion about how old you are. From 18 August, OpenAI began rolling out “age prediction” on its consumer plans: a system that guesses whether your account belongs to someone under 18 and, if it decides you’re a minor, quietly switches you into a restricted version called ChatGPT for Teens. You are not asked. If the guess lands on “teenager,” the guardrails go up by default. Answer first, because the mechanism matters more than the alarm: the guess is behavioural, and it is admittedly imperfect. By OpenAI’s own account the system reads “general topics you talk about, the times of day you use ChatGPT, how and when your account is used, and how long your account has existed.” And the way to make it stop guessing is not a toggle. It is to prove your age to a third-party verifier called Persona, with a live selfie, a government ID, or both. The choice on offer isn’t whether to be identified. It’s how. None of this arrives from nowhere, and we’ll be fair about why in a moment. But a change that infers a protected characteristic from the content of your conversations, applies real restrictions on the strength of a guess, and offers identity verification as the only exit is worth reading slowly — especially for the adults who will be misclassified, because OpenAI says plainly that some will be. What OpenAI actually switched on The launch has two parts. The visible one is ChatGPT for Teens , announced on 18 August: a version with study-focused features and stronger safety defaults for under-18s. The consequential one is age prediction , the system that decides who gets dropped into it. In OpenAI’s words, “If our system estimates someone is under 18 or they state their age is between 13 and 17, they are automatically placed into ChatGPT for Teens.” It is rolling out globally, with the EU following “in the coming weeks” to fit regional rules. What does the teen experience act

📝 Originally published (in Japanese) at forge.workstyle.tech . In a previous article , we introduced an environment for parallel execution of coding agents using Git worktrees. This article is a follow-up. As we progressed with parallelization, we ended up with 3-5 Claude Code sessions simultaneously developing the same microservices . What happened was no longer just "parallel execution of tools" but actual "team development" . All the issues that arise in human teams—miscommunication, deployment conflicts, and territorial overlaps—occur here as well. And the practices that work for human teams work almost identically here. We’ll share seven recipes that emerged from actual operations, along with real-life close calls. Real-Life Story: Averting a Deployment Rollback Disaster at the Last Minute One day, while Session A (responsible for voice functionality) was in the middle of a major refactor, Session B (responsible for streaming functionality) sent this message: "We’re about to build the frontend as version 1.0.399 (based on main)." At first glance, this seemed fine. However, in this repository, the authoritative branch for the production environment was not main but a dedicated deployment branch . The latest features from the past few dozen versions were only in the deployment branch, while main was outdated. If Session B had deployed an image based on main, weeks’ worth of features would have been rolled back in production . Session A immediately sent a warning, and Session B halted the build before pushing. Session B then cherry-picked their changes into the deployment branch and rebuilt the image, avoiding the disaster entirely. All this communication was handled autonomously between the agents via session-to-session messages . I (the human) only learned about it later from the logs. This incident highlights two things: parallel agents can cause the same accidents as human teams , and with proper communication channels and rules, they can prevent accidents jus

We are living in an era where our wearable devices know more about our physiological state than we do. My Oura Ring knows I stayed up too late binge-watching The Bear , yet my Google Calendar still insists I have a "High-Intensity Interval Training" (HIIT) session at 8:00 AM. This disconnect is where injuries happen and burnout begins. In this tutorial, we are building a Digital Twin Health Agent —a sophisticated AI Agent using LangGraph and Healthcare Automation to bridge the gap between bio-data and action. By the end of this guide, you’ll have a system that reads your recovery scores, reschedules your workouts, and even orders magnesium supplements when your sleep quality drops. This is the future of Digital Twin technology applied to personal wellness. 🚀 The Architecture: A Feedback Loop for Your Body Unlike a simple linear script, a health agent needs to maintain state and make conditional decisions. If your recovery is 90+, push hard; if it's below 50, swap that CrossFit session for Yoga. Here is how the data flows through our LangGraph state machine: graph TD A[Start: Morning Trigger] --> B{Fetch Oura Data} B --> C[Analyze Recovery Score] C --> D{Is Score < 60?} D -- Yes --> E[Reschedule Google Calendar to 'Rest/Yoga'] D -- No --> F[Confirm High-Intensity Workout] E --> G[Check Nutrient Deficiencies] F --> H[End Loop] G --> I{Low Magnesium/Sleep?} I -- Yes --> J[Draft Instacart Order] I -- No --> H J --> H Prerequisites To follow this advanced guide, you'll need: LangGraph & LangChain : For orchestration. Oura Cloud API : Access to your readiness/sleep data. Google Calendar API : To modify your schedule. Python 3.10+ Step 1: Defining the Agentic State In LangGraph, everything revolves around the State . We need to track our physiological metrics and our current calendar status. from typing import TypedDict , List , Annotated from langgraph.graph import StateGraph , END class HealthState ( TypedDict ): recovery_score : int sleep_quality : str current_schedule