How Jev works starts with a simple observation: most software does not need an AI system to write an essay. It needs a dependable answer to a bounded question. Should this support ticket be escalated? Which queue owns it? Is the document complete? Should an agent stop and ask a person for help? That is […]
How Jev works starts with a simple observation: most software does not need an AI system to write an essay. It needs a dependable answer to a bounded question. Should this support ticket be escalated? Which queue owns it? Is the document complete? Should an agent stop and ask a person for help? That is […]
How Jev works starts with a simple observation: most software does not need an AI system to write an essay. It needs a dependable answer to a bounded question. Should this support ticket be escalated? Which queue owns it? Is the document complete? Should an agent stop and ask a person for help? That is […]
How Jev works starts with a simple observation: most software does not need an AI system to write an essay. It needs a dependable answer to a bounded question. Should this support ticket be escalated? Which queue owns it? Is the document complete? Should an agent stop and ask a person for help? That is […]
Choose any positive whole number. If it is even, divide it by 2. If it is odd, multiply it by 3 and add 1. Then repeat. This is the Collatz conjecture, one of mathematics’ most famous open problems. The rule can be followed with a calculator, a spreadsheet, or a few lines of code. Yet […]
Choose any positive whole number. If it is even, divide it by 2. If it is odd, multiply it by 3 and add 1. Then repeat. This is the Collatz conjecture, one of mathematics’ most famous open problems. The rule can be followed with a calculator, a spreadsheet, or a few lines of code. Yet […]
Choose any positive whole number. If it is even, divide it by 2. If it is odd, multiply it by 3 and add 1. Then repeat. This is the Collatz conjecture, one of mathematics’ most famous open problems. The rule can be followed with a calculator, a spreadsheet, or a few lines of code. Yet […]
Games made with Astra are moving beyond the one-line “make me a game” prompt. The useful question is whether an AI agent can participate in a real development loop: interpret a brief, edit a project, run it, observe a fault and make a bounded repair. The early evidence is interesting, but it needs careful reading. […]
People searching for how to build games with Astra are usually not looking for a one-line prompt that magically produces a finished game. They want a practical route from an idea to something playable: a small scene, clear rules, a browser build, and a way to test what changed. That is the useful AI game […]
AI agent evaluation starts with a simple reality: an agent can produce a polished final response and still fail the task. Once an AI system can retrieve information, call tools, request approval and change an external system, the final text is only a report of what happened. It is not proof that the work was […]
Time to first token (TTFT) explains a familiar LLM behaviour: a noticeable pause before the first word, followed by a stream of much faster tokens. If a model needs 1.5 seconds to begin but delivers later tokens roughly every 30 ms, the gap is usually the result of how transformer inference works, not simply a […]
One FastAPI tool I’ve found genuinely useful lately is FastCRUD. More specifically, it is a practical way to reduce the repetitive work around FastCRUD for FastAPI projects that use SQLAlchemy. When you are building a conventional API with FastAPI and SQLAlchemy, the repeated work adds up quickly. Every new resource can mean creating a record, […]
AI product development has changed the cost of making software. A product idea that once needed a specification, design work, and an engineering sprint can now become an interactive prototype in hours. An engineering task that might have taken days can be explored, implemented, reviewed, and revised much faster with coding agents. That is a […]
LangGraph evaluation turns a convincing multi-agent demo into a workflow you can improve with evidence. A system may return a plausible answer while choosing the wrong specialist, calling unnecessary tools, or becoming too slow and expensive for real users. This tutorial shows how to measure those failures before they reach production. In the previous lessons, […]
LangGraph persistence is what makes a multi-agent workflow safe to pause, inspect, and continue later. Without it, a process restart, a human review step, or a temporary failure can mean starting the whole graph again—and possibly repeating work that already happened. In the previous tutorial on routing and parallel execution, a router selected specialist agents […]
AI product development now has a much cheaper first step: turning an idea into a small, interactive prototype. What has not become cheap is the judgment required to decide whether that idea should become a real product. A rough workflow that once needed a specification, design work and an engineering sprint can now be explored […]
LangGraph routing is the point where a multi-agent graph stops broadcasting work and starts making deliberate choices. It decides which specialist should handle a request, which tasks can run at the same time, and when their findings are ready to combine. In the previous tutorial on LangGraph shared state, we defined safe information flow. Here, […]
LangGraph shared state is where a multi-agent workflow becomes predictable. A supervisor can choose the right specialist and still produce a weak answer if every worker receives the same noisy conversation history. The next step is deciding what each agent can read, what it may return, and what must remain private. This tutorial continues the […]
Python 3.15 is close enough to matter now. The second release candidate arrived on 1 September 2026, and the core team plans the final release for 1 October. That leaves developers a useful window: understand the changes, test the ones that touch your code, and upgrade with fewer surprises. This release does not depend on […]
LangGraph Supervisor Tutorial · Multi-Agent SystemsPart 2 of 6 · View the series guide ← Part 1: Build a conversational agent with LangGraph A LangGraph supervisor tutorial should begin with a simple idea: a useful multi-agent system is not a group of bots talking at random. It is a workflow with a clear decision-maker. One […]
This LangGraph multi-agent systems tutorial series takes you from a single conversational agent to a reliable workflow of a supervisor and specialist agents. Each tutorial adds one practical capability: delegation, shared context, routing, recovery, or evaluation. Who this series is for This series is for Python developers who understand the basics of language models and […]
GPT-6 Astra has not broken the rules of AI system design. It has made the limits of those rules much harder to ignore. OpenAI’s new model is built for work that stretches across browsers, files, code, research, and professional software. It can plan, use tools, inspect results, adapt to new instructions, and continue through a […]
iPhone 18 Pro Max · camera settings guide Light.With room to choose. Explore iPhone 18 Pro Max camera settings for the photograph you want to make — with aperture, focal length, shutter speed and ISO shown beside every example. A face lit by red and blue. Three people standing at different depths. Sunlight catching an […]
If you are comparing MCP vs API, the short answer is that they solve different integration problems. APIs expose capabilities; MCP gives AI applications a standard way to discover and use tools and context. Key takeaway: An API is a contract for interacting with a system. MCP is a protocol that helps AI applications discover […]
How the em dash became a reason to distrust writing, and what deserves our attention when we edit. Imagine finishing a thoughtful post, reading it back, and making one last change. You delete a dash. The sentence worked perfectly well. You just don’t want someone in the comments saying, “Obviously written by AI.” That small […]
I have been following the rapid progress of AI-assisted software development, and games with Astra caught my attention because they show a different side of what coding agents can do. OpenAI’s recent developer post demonstrates how an AI coding agent can work across architecture, rendering, testing, assets, and optimisation rather than simply generating isolated code. […]
When OpenAI President Greg Brockman took the stage to unveil GPT-6 Astra, he did not hedge his words: “Welcome to the AGI era.” It is the kind of statement that makes Silicon Valley stop in its tracks. However, beneath the slick announcement and leaked feature flags, a stranger and higher-stakes story is unfolding. OpenAI has […]
Interview question:Uber predicts that ride demand in a particular area will increase significantly in the next 20 minutes. How would you design an Uber demand prediction system that forecasts demand and proactively encourages drivers to move to that area? This is a classic marketplace and machine-learning system design question. The interviewer is testing whether you […]
AI agent frameworks, including Vercel AI SDK, LangGraph, CrewAI, AutoGen and LangChain, are often grouped together. However, they do not solve the same problem. Choose from the architecture you need to build rather than searching for one “best” framework. This comparison uses the supplied five-tool overview as a research prompt, not as a source of […]
When new AI provenance signals appear in AI tools, the online reaction can feel predictable: a new lock is announced, then attention turns to whether someone can pick it. That framing misses the more useful question. What can a provenance signal actually tell a reader? The short answer is that it can add valuable context […]
Ninety four thousand block reads for one row, by its UUID primary key. I read that line three times, half convinced I had pasted the wrong output into my own terminal. That query used to come back in under 20 milliseconds. At 200 million rows, it took almost four seconds. The only reason anyone noticed […]
The fastest way to get value from a personal AI agent is to stop imagining an AI employee. Start with one routine that is mildly annoying, happens often, and produces work you can easily review. In a couple of hours, you can prototype a useful workflow. You cannot reasonably expect a dependable, unattended assistant that […]
An agent evaluation flywheel can expose the path an AI agent took to reach an answer. A final-answer score can hide wrong tools, needless retries, weak evidence, or a failure to adapt when new information appears. That creates a practical question: what should change next? This post turns that diagnosis into a five-stage operating model. […]
Trajectory evaluation shows how a final answer can look perfect while the agent behind it has already failed. Imagine an agent that tells a support team: “The customer record has been updated.” The sentence is clear and reassuring. However, the trace may show a different story. It may select a search tool instead of an […]
Vector quantisation helps when many groups of values look alike. An image may contain similar patches of pixels, a signal may repeat familiar shapes, and a collection of feature vectors may cluster around a few common patterns. As a result, storing every group in full can be wasteful when an approximate representation is acceptable. In […]
If by “turbo vec” you mean the project source, it is a project aimed at a familiar AI-infrastructure problem: storing and searching embedding vectors without treating memory, storage, and retrieval quality as afterthoughts. Its repository describes TurboVec as a vector index built on TurboQuant, written in Rust, with Python bindings. That description is useful, but […]
Contextual Retrieval addresses a common RAG failure: a system can return a chunk that looks relevant while still missing the information the user needs. The usual culprit is lost context: a sentence survives chunking, but the document, product, customer, date, or definition that makes the sentence meaningful does not. Anthropic’s answer is Contextual Retrieval. In […]
This multi-agent workflow roadmap introduces coordinated AI roles and workflows working toward one outcome—not magical, fully autonomous AI teams. It is the starting point for the series, and it will become a linked learning path as each tutorial is published. Key takeaways A multi-agent system divides a broader job among defined AI roles and workflow […]
A feature request looks small until it crosses every layer of an application. Clean Architecture with SOLID helps when a checkout rule touches an HTTP handler, ORM model, pricing calculation, email notification and message consumer at once. It separates the business decision from the machinery used to deliver and store it. Clean Architecture is not […]
Python is a readable, general-purpose programming language used for automation, web development, data analysis, and artificial intelligence. This Python beginner guide explains why the language is so approachable, how to install it, and how to run your first program with confidence. Why learn Python? Python is designed to be easy to read. Its clear syntax […]
Python is a popular programming language that is known for its simplicity, readability, and versatility. In this post, we’ll cover the fundamental concepts of Python programming that will help you get started with building your own programs. Data types and Variables In Python, there are several data types that you can work with, including integers, […]
Unlocking the Puzzle of Sorted Arrays When programming intersects with problem-solving, each line of code we write is more than just instruction; it’s a strategic move in a grander game of logic and efficiency. Big names in tech, such as Google, Apple, and Microsoft, recognize this. They often challenge interviewees with problems that seem deceptively […]
In the realm of machine learning (ML), the rise of MLOps (Machine Learning Operations) represents a paradigm shift towards greater efficiency and streamlined workflows. MLOps is a set of practices that aims to unify ML system development and ML system operation. It focuses on automation and monitoring throughout the entire machine learning lifecycle, facilitating smoother […]
Some models are too simplistic and ignore important relationships in the training data, which could have improved their predictions. Such models are said to have high bias. When a model has high bias, its predictions are consistently off, at least for certain regions of the data if not the whole range. For example, if you try […]
The decision tree algorithm is one of the widely used methods for inductive inference. It approximates discrete-valued target functions while being robust to noisy data and learns complex patterns in the data. The family of decision tree learning algorithms includes algorithms like ID3, CART, ASSISTANT, etc. They are supervised learning algorithms used for both, classification […]
An ensemble methods are technique which uses multiple independent similar or different models and then combine them to produce improved results. Ensemble methods usually produces more accurate solutions than a single model would. The motivation for using ensemble models is to reduce bias or variance. For e.g. A random forest is an ensemble of multiple decision […]
Classification vs Regression Classification predictive modeling problems are different from regression predictive modeling problems. Classification is the task of predicting a discrete class label. Regression is the task of predicting a continuous quantity. There is some overlap between the algorithms for classification and regression; for example: A classification algorithm may predict a continuous value, but […]
Welcome to a journey through the delicate landscape of machine learning models! Today, we’re tackling two notorious pitfalls: underfitting and overfitting. Imagine you’re teaching a child to recognise animals. If you only show them pictures of small dogs, they might not recognise a large dog as a dog—that’s underfitting. The model is too simplistic and […]
In all three cases, the variance increases and the bias decreases as the method’s flexibility increases. However, the flexibility level corresponding to the optimal test MSE differs considerably among the three data sets, because the squared bias and variance change at different rates in each of the data sets. In the left-hand panel of Figure, […]
Web-scraping is an vital strategy, as often as possible utilised in a part of distinctive settings, particularly information science and information mining. Python is to a great extent considered the go-to dialect for web-scraping, the reason being the batteries-included nature of Python. With Python, you’ll be able make a basic scratching script in approximately 15 minutes and in beneath 100 lines of code. So regardless of utilisation, web-scraping could be a expertise that every Python software engineer must have beneath his belt. Before we begin getting hands-on, we ought to step back and consider what […]
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