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For a few glorious years, a $399 portable gadget could run almost anything you'd want to play. In 2022, the Steam Deck finally made PC gaming portable and affordable. I played through the vast majority of Elden Ring on a Steam Deck, agape that such a rich world could comfortably fit between my two hands. […]

AI Glossary: A to Z An A-to-Z glossary of AI terms, created with help from AI itself. Because in 2026, the best way to study AI is apparently to ask AI itself. 🤣 Written for beginners and practitioners alike. Each term includes a plain English definition and a real-world example. Quick Navigation A · B · C · D · E · F · G · H · I · J · K · L · M · N · O · P · Q · R · S · T · U · V · W · X · Y · Z ↑ Back to top A Term Definition Example Agent (AI Agent) An AI system that perceives its environment, makes decisions, and takes autonomous actions to achieve a goal A coding agent that writes, runs, and debugs its own code without human intervention AGI (Artificial General Intelligence) A hypothetical AI that can match or exceed human-level intelligence across any task — does not yet exist Often cited as a long-term goal by companies like OpenAI and DeepMind AI (Artificial Intelligence) The field of computer science focused on building machines that can perform tasks normally requiring human intelligence ChatGPT writing an essay, an algorithm detecting cancer in X-rays AI Ethics The principles and practices for developing and deploying AI in ways that are fair, transparent, and safe Auditing a hiring algorithm to ensure it doesn't discriminate by gender or race AI Safety The field dedicated to ensuring AI systems remain reliable, controllable, and beneficial as they grow more capable Research into preventing AI from pursuing goals that harm people Alignment The challenge of ensuring an AI system's goals and behaviour match what its designers and users actually intend Preventing a powerful AI from optimising for a metric in a way that causes unintended harm Annotation The process of labelling raw data so it can be used to train supervised learning models Humans drawing bounding boxes around cars in images to train a self-driving model API (Application Programming Interface) A defined interface that lets software systems communicate with each other Calling the OpenAI API to

submitted by /u/Xaneris47 [link] [留言]

Cloudflare recently added support for Claude Managed Agents, allowing developers to run and manage Claude agents within Cloudflare. Developers can connect agents to private systems, choose their runtime environment, and monitor agent activity using Cloudflare services. By Renato Losio

When you build a PowerApps Component Framework (PCF) component that will be used across multiple geographies, need to serve labels, button captions, validation messages, and tooltips in the user's preferred language. PCF has a built-in answer based on .resx resource files, the same format used by .NET applications. The mechanism is elegant in production — but surprisingly tricky during local development. This walkthrough takes you through the full setup, step by step, and then explains a problem that arises while locally debugging your PCF. Step 1 — Create the strings folder and your first .resx file PCF expects your localized strings to live in a folder (the conventional name is strings ) inside your component directory. Each language gets its own file, named with the pattern: <ComponentName>.<LCID>.resx The <LCID> part is the numeric Locale ID , not the textual code ( en-US , it-IT ). The framework relies on this naming convention to identify which file to load for a given user. Common LCIDs: Language LCID English (en-US) 1033 Italian (it-IT) 1040 German (de-DE) 1031 French (fr-FR) 1036 Spanish (es-ES) 3082 Japanese (ja-JP) 1041 Chinese Simplified (zh-CN) 2052 Portuguese (pt-BR) 1046 For a component called EquipmentGrid , the structure looks like this: EquipmentGrid/ ├── ControlManifest.Input.xml ├── index.ts └── strings/ ├── EquipmentGrid.1033.resx ├── EquipmentGrid.1040.resx └── EquipmentGrid.1031.resx Tip: Always include 1033.resx (English). The PCF runtime falls back to the first <resx> declared in the manifest when the user's preferred language isn't available, and English is the safest default. Step 2 — Author the resource file content A .resx file is just XML. Here's a minimal Italian version ( EquipmentGrid.1040.resx ): <?xml version="1.0" encoding="utf-8"?> <root> <resheader name= "resmimetype" > <value> text/microsoft-resx </value> </resheader> <resheader name= "version" > <value> 2.0 </value> </resheader> <resheader name= "reader" > <value> System.Resou

TLDR Monitoring AI agents in production requires distributed tracing: a single user request fans out into 10 or more internal operations, and logs alone cannot show you which step is slow, failing, or burning your token budget. OpenTelemetry's gen_ai.* semantic conventions give you standardized span attributes for LLM calls, tool invocations, and agent steps. Some are stable today; others are still experimental. Auto-instrumentation libraries (OpenLLMetry, OpenInference, OpenLIT) cover most agent frameworks with two to three lines of initialization code. You do not change your agent code. Traces ship to OpenObserve over OTLP. From there you get SQL-queryable trace data, token usage dashboards, cost attribution by agent and model, and alerting on latency and cost anomalies. OpenObserve also exposes an MCP server. You can query your live agent traces from a Claude or GPT session without opening a dashboard. Why Agents Are Harder to Monitor Than a Single LLM Call A single LLM call is straightforward to observe. One HTTP request, one response, one latency number. You can log the input and output and call it done. An agent is different. When a user sends a message, the agent calls an LLM to decide what to do, invokes a tool, processes the result, calls the LLM again, possibly calls another tool, and eventually returns a response. That one user message becomes ten or more internal operations. Some of those operations call external APIs. Some retry. Some spawn sub-agents. Without distributed tracing, you see none of this structure. You know the response took 8 seconds. You do not know whether the LLM took 7 of those seconds or whether a tool made three retries before timing out. Four categories of problems appear in production agents that you cannot debug without traces: Latency. Which step is slow? The LLM call? The tool execution? A retry loop the agent entered because the tool returned ambiguous output? Cost. Which agent, which task, which model is consuming tokens? A s

Last year, I was doing something that felt increasingly absurd: manually reading AI-generated content to decide if it was "good enough." PostAll — the content automation tool I've been building — was producing hundreds of blog posts per week for clients. And I had no systematic way to evaluate quality at scale. I was spot-checking. Vibes-checking, really. That doesn't work at volume. So I built a programmatic quality analysis pipeline, ran it over 1,000 AI-generated posts, and let the numbers tell me what my gut was missing. The findings surprised me. A few of them genuinely changed how I think about AI content quality. What I Actually Measured First, a definition of terms, because "quality" is almost meaninglessly vague in this space. I broke quality into five measurable dimensions: Readability — Flesch-Kincaid grade level and reading ease score Keyword density — Target keyword frequency and distribution across the post Grammar error rate — Errors per 1,000 words, caught via LanguageTool's API Factual accuracy — Claims that could be verified programmatically (dates, statistics, named entities cross-referenced against a knowledge base) Structural consistency — Presence of expected elements: intro hook, subheadings, conclusion, CTA I used 1,000 posts across three categories: SaaS product descriptions, long-form "how-to" articles (1,200–2,000 words), and listicles (500–900 words). All were generated by PostAll using GPT-4o, with various prompting strategies. The Setup The analysis pipeline isn't complicated, but the piece that makes it useful is the batch processing layer: import anthropic import language_tool_python import textstat from dataclasses import dataclass from typing import Optional import json @dataclass class QualityReport : post_id : str flesch_reading_ease : float flesch_kincaid_grade : float grammar_errors_per_1000_words : float keyword_density : float structural_score : int # 0–5 based on element presence flagged_claims : list [ str ] overall_score :

"Settlement layer for the agent economy" is suddenly a crowded sentence. In the last few weeks, two very different things have started competing for it. OKX Agent Payments Protocol (APP) announced in May 2026 — backed by AWS, Alibaba Cloud, Uniswap, Paxos, QuickNode, with ecosystem support from Base, Ethereum Foundation, Solana, Sui, Aptos, Optimism — describes itself as the settlement layer where AI agents pay each other. AEON's $8M pre-seed (May 20, YZi Labs) described itself the same way. There is a list now, and it is getting longer. We build one of the things on this list, and we think the framing is wrong. These are not rivals jostling for the same slot. They are two different layers, and they answer two different versions of the same question: what does an agent need to settle a trade? This piece walks through the two layers, where each is the right answer, and why an honest comparison gets you further than picking sides. What APP and other custodial-venue protocols actually do A useful way to read OKX APP — and similar moves coming from the larger exchanges — is to treat them as a venue layer . An exchange already holds inventory across many chains. It already has the risk engines, the liquidity, the legal arrangements with banks and partners. Adding an agent-facing API on top of that machinery is a relatively short walk. What an agent gets, in exchange, is breadth. Many chains, many assets, batched and netted settlement against deep internal books, and fast execution because the exchange is just moving entries in its own ledger. For an agent whose job is "find a price somewhere and execute now," that is a powerful primitive. What the agent gives up, in exchange, is a counterparty. At any moment between deposit and withdrawal, the agent's balance is the venue's promise to pay. That promise is normally good. The agent has no way to verify, from inside its own logic, that it still is. This is not a criticism of OKX APP. It is the structural shape of any venue-

The Problem We Were Actually Solving During load testing at 50k concurrent hunters hitting the hunt endpoints, p99 latencies stayed under 200ms. But at 270k concurrent users in production, the hunt page suddenly took 1.8 seconds to load, triggering cascading 502s from our CDN. The error surfaced in Datadog as hunt_page_render_time_bucket{le=2.0} = 42% while le=0.5 dropped to 18%. The fingerprints were identical across three regions: high latency correlated exactly with Redis cache miss rate spiking from 12% to 48% during the hunt start window. Our cache-aside pattern with a 30-second TTL was amplifying miss storms. We discovered that the treasure hunt start time was synchronised by marketing campaigns. When the clock struck 10:00:00 UTC, 270k users hit the endpoint within 30 seconds. Each request would check the cache (miss), fetch from PostgreSQL, render the page, and write the cache entry. But PostgreSQL couldnt keep up with 9k queries per second during that window, causing query queueing and connection exhaustion. The Redis layer, designed for 150k ops/sec, was not the bottleneck. The database was. What We Tried First (And Why It Failed) Our first attempt was to increase Redis TTL from 30 seconds to 5 minutes. This reduced cache misses from 48% to 24%, and p99 latency improved to 650ms. But at 320k concurrent users, the latency still spiked to 1.4s because the underlying database queries were still hitting the same table with the same indexes. The Redis layer was masking symptoms, not solving the root cause. Next, we tried database read replicas. We spun up three read replicas and routed hunt queries to them using a weighted service mesh. This worked for a few minutes, but then we hit replication lag. The replicas fell 800ms behind primary, causing hunt pages to display stale treasure locations. Our operators started getting customer complaints about seeing the wrong treasure coordinates. We rolled back within 15 minutes. We even tried increasing PostgreSQL share

[ Removed by Reddit on account of violating the content policy . ] submitted by /u/Economy_Goose_8561 [link] [留言]

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I Built Last...

A while ago I tried to build a local coding assistant. I downloaded Qwen3, fired it up on my MacBook with 16GB of RAM, and within a day realized the output quality was nowhere close to Claude or GPT-5. The model could fit . It just couldn't compete . So I changed the question. If I can't make the model smarter on my hardware, can I make what I feed it smarter? Where the tokens actually go I started watching where my Claude / Cursor / Copilot sessions actually spent their tokens. The surprise: most of it wasn't reasoning. It was lookup . Every fresh chat about my company's database re-discovered the same things: What does status = 3 mean? (cancelled) How does orders join to users ? ( orders.user_id → users.id ) What's that cryptic JobStatus enum? (a dozen integer codes nobody remembers) The model figured it out, the session ended, and tomorrow it figured it out again . Same tokens, same latency, every single time. The expensive part of working with an AI wasn't the thinking — it was re-teaching it things it had already learned yesterday. There's a lot of attention right now on trimming AI output tokens (talk like a caveman, strip the pleasantries, etc.). But in my workflow the bigger leak was on the input side: paying full token cost every session to re-establish context that never changed. "Memory" isn't a feature, it's an architecture question AI clients are starting to bolt on "memory" features. But they're proprietary, opaque, and locked to one tool. Claude's memory doesn't help Cursor. Cursor's doesn't help Copilot. You can't inspect it, you can't share it with a teammate, and you can't diff it. What I actually wanted was an explicit, inspectable, shareable context layer that any AI client could read deterministically — same answer every time, same file my team could hand off. I picked the highest re-learn cost in my world to start with: SQL databases. Enter amnesic amnesic is an open-source MCP server that gives any AI client persistent semantic memory of your

Most trading ideas start as vague thoughts. "Buy when RSI is oversold and price bounces from support." It sounds reasonable. But the moment you try to test or automate it, the ambiguity becomes obvious. What exactly counts as oversold? How is support defined? What qualifies as a bounce? When do you exit? Without precise answers, the idea cannot be tested, measured, or executed consistently. This gap between intuition and execution is exactly what no-code strategy builders are designed to close. Why vague trading ideas fail Most traders think in concepts rather than rules. "Buy the dip." "Trade strong momentum." "Enter when the trend looks healthy." These ideas feel intuitive, but they are unusable in practice unless translated into explicit logic. Without clear definitions, you cannot backtest a strategy, cannot repeat decisions consistently, and cannot diagnose why results change over time. Ambiguity leads to second-guessing. Second-guessing leads to inconsistent execution. Inconsistent execution makes performance impossible to evaluate. What a no-code strategy builder actually does A no-code strategy builder is a visual system that forces clarity. Instead of writing code, you select indicators, define conditions, combine logic using AND/OR rules, specify entries and exits, and then test the strategy on historical data. Conceptually, it works like assembling building blocks. Each block represents a condition such as "RSI below 30" or "price above moving average." When combined, those blocks form a complete, testable trading system. The key benefit is precision. From idea to testable strategy The transformation follows a predictable workflow. You begin with a loose idea, such as buying when a stock is oversold and starting to recover. You then break that idea into components. What defines oversold? What signals recovery? How do you enter? How do you exit? How much do you risk? Once those questions are answered, the idea becomes a set of explicit rules. For example,

🚀 The Golden Age of “Just Ship It” A few months ago, I started building side projects differently. Instead of: Planning architecture Reading documentation Writing every function manually I started doing this: “Build me a responsive dashboard with authentication, dark mode, PostgreSQL integration, and Stripe payments.” And somehow… It worked. AI tools can now generate: APIs UI components Database schemas Docker configs Tests Documentation We’ve entered the era of vibe coding . And honestly? It feels amazing. What Is “Vibe Coding”? Vibe coding is when you: Describe what you want Let AI generate most of the implementation Keep iterating through prompts Instead of engineering every detail manually, you steer the vibe of the application. Tools making this popular: Cursor GitHub Copilot Claude Windsurf ChatGPT Replit AI You become less of a code writer and more of a: reviewer editor product thinker debugger At least in theory. The First Few Days Feel Like Magic The productivity boost is unreal. You can build in hours what used to take days. Things that once required: Stack Overflow endless documentation tabs debugging sessions at 2 AM …now happen through prompts. You feel unstoppable. Then Production Arrives And production is where the vibes end. Because production doesn’t care if the demo looked cool. Production cares about: edge cases reliability security scalability maintainability observability This is where AI-generated code starts exposing cracks. Problem #1: The Code Looks Right This is the dangerous part. AI code is often: clean formatted nicely modern-looking confident But hidden underneath: unnecessary complexity duplicated logic subtle bugs bad abstractions Problem #2: Hallucinated Architecture AI is very good at generating: components snippets isolated features It is much worse at: long-term architecture consistency scaling systems over time You start noticing: 4 different API patterns duplicated utilities random folder structures inconsistent state management

I am responsible for a research project that is supposed to train a GPT-like model (Transformer-decoder) with 100M, 250M and 500M model variants. # params ## training dataset - 750M tokens - vocabulary is ~15k to ~100k tokens (depends on tokenizer settings) - ~3% of the vocabulary is used in ~50% of the training tokens (similar to language, where most of the vocabulary is used very sparsely) ## training hyper-params - optimizer = AdamW - lr = 1e-3 (works the best compared to 1e-2 and 1e-4) - betas = [0.9, 0.95] - effective batch size = 4M tokens - epoch = 16 - warmup steps ~200 (approx 1 epoch) ## model hyper-params - 16 layers (but variants with up to 48 layers were tested) - embedding = flexible to yield 100M, 250M and 500M model - MLP size = 4*n_embd - 16 attention heads - context window = 1000 # Issue The model seems to fail to learn the basic auto-regressive behavior. It often gets stuck on generating a single token (no repetition penalty, no sampling yet). Is training GPT-like models still a black magic? Is there some trick to this? *Disclaimer*: I will add/edit the parameters above as people ask clarifying questions. submitted by /u/gartin336 [link] [留言]

The advice was correct. That's what makes it interesting. A creator with a large audience recently described the problem precisely: unused project ideas atrophy. They gave the prescription: externalize the idea, commit to a 30-60-90 day sprint, get into a community that holds you accountable, treat a deployed URL as the only real milestone. The audience listened. The ideas stayed unshipped. Not because the advice was wrong. Because advice is not a mechanism. The gap between diagnosis and structure There's a category of knowledge that's completely useless without enforcement. "You should exercise consistently." Correct. Also irrelevant for the 80% of people paying for gym memberships they don't use. "You should ship your side project in 30 days instead of perfecting it." Also correct. Developers have been hearing this for years. The projects that were "almost done" last year are still almost done. The advice identifies the problem. The problem persists. The gap between them is not information. It's structure. Discipline is the tax on misalignment One phrase from the transcript stayed with me: "Discipline is the tax on misalignment." The insight is sharper than it sounds. When what you're building doesn't connect to why you're building it, every work session requires a new act of will. You're not building forward momentum — you're paying an interest payment on a debt you haven't quite defined. This is why most sprint systems fail. They give you the structure (30 days, daily tasks, accountability partner) but skip the alignment check. The structure holds for two weeks. Then it becomes another system you're "almost following." What the AI makes worse Here's where it gets specific for developers using AI tools on side projects. The AI is genuinely useful. It generates architectures, writes boilerplate, outlines features, summarizes where you are. The output looks like forward motion. But the AI has no ground truth about your actual progress. It has your files and your pr

Introduction In my previous IAM article we learnt basics of IAM and how to create Users, Groups and attach Policies. You can refer here: https://dev.to/kadhamvj23/aws-identity-and-access-management-explained-for-beginners-cn7 After setting up secure access to our AWS account using IAM, the next question we mostly have is where do we actually run our application? The answer is Amazon EC2 - Elastic Cloud Compute. EC2 is one of the most widely used AWS services and understanding it well is essential for anyone starting their cloud journey. In this article we will cover what EC2 is, why it exists, the different types of instances, pricing models, Regions and availability Zones and finally hands-on walk through of creating your first EC2 instance. Breaking Down the Name -EC2 Let us understand what each word in the name actually means: Elastic --> In AWS you will notice many services have this prefix "Elastic". The reason is simple. Whenever AWS provides a service that can be scaled up or scaled down based on our needs, that service is called Elastic . With EC2 you can increase resources when traffic is high and decrease them when the traffic is low. So in simple terms EC2 = A virtual server on the cloud that you can resize anytime. Cloud: EC2 runs on AWS's public cloud infrastructure, meaning the servers are owned and managed by Amazon across the world. Compute: The word compute means you are asking AWS to provide you CPU, RAM and Disk - basically a virtual machine or server that can run your applications. How does EC2 actually work? When you request a Virtual server from AWS, here is what happens behind the scenes: You request a virtual machine on AWS ⬇️ request goes to a Hypervisor(a software layer sitting on top of physical servers that creates and manages VMs) ⬇️ Hypervisor creates your VM ⬇️ You get the access to your EC2 instance You never touch any physical hardware. AWS manages all of that for you. Why use EC2? Imagine your company wants to host an application. T

Blog post: https://wezteoh.github.io/posts/diffusion-for-sketch-guided-trajectory-simulation/ During NBA games, coaches often sketch attacking plays on a whiteboard and mentally simulate how teammates and defenders might react. In this project, I explored using diffusion models for controllable basketball trajectory simulation. Instead of only forecasting future trajectories, the model generates gameplay conditioned on partial “sketches” of player movement instructions. One interesting aspect is that diffusion models refine all player trajectories jointly, which makes sketch-conditioned simulation feel more natural compared to autoregressive generation. I wrote up the methodology, experiments, and implementation details in the link above. Code and model are fully open sourced as well. Curious to hear thoughts from others working on generative modeling, trajectory prediction, or sports analytics. submitted by /u/part-time-delver [link] [留言]

Last.fm is now an independent company, nearly two decades after it was acquired by CBS.

The Problem We Were Actually Solving Treasure hunts in Hytale arent just about generating loot. Theyre about generating simultaneous loot across thousands of players while keeping the world state consistent. We started with the assumption that events are stateless notifications: a hunt starts, we fire an event, clients react. That model worked fine when we had 200 concurrent players. At 2,000 players, the event bus turned into a 40 MB/s firehose of JSON blobs. Each loot drop required serializing the entire chunk state—blocks, entities, metadata—so clients could render the drop in real time. The JVMs G1GC couldnt handle the allocation rate. Every 47 minutes, a GC cycle would pause for 4.2 seconds, the chunk cache would fragment, and the server would hard crash with an OutOfMemoryError in net.minecraft.server.MinecraftServer#processQueue. The real problem wasnt the hunt logic. It was the architectural laziness of treating events as a catch-all glue layer instead of a boundary layer with explicit interfaces. What We Tried First (And Why It Failed) We tried Kafka as the event bus. The plan was to shard hunts by region and stream loot drops as compacted topics. The first run worked for about 6 hours before the compacted topics started to bloat. Each hunt was generating 700 KB of serialized chunk state per drop. At 30 drops per hunt per minute, thats 21 MB per hunt per minute. With 400 active hunts, the brokers couldnt keep up. The lag grew to 12 seconds, clients started rubber-banding, and we got a flood of Discord reports: You sank my boat! The event stream was now the bottleneck, not the event source. Next, we tried Redis Streams with a Lua script to aggregate loot drops per chunk. Within 30 minutes, we hit the 4 GB maxmemory limit because Lua scripts were stacking dropped items in memory while waiting for the next batch. The script was elegant—O(1) per drop—but the memory footprint made it unusable in production. Finally, we tried a sidecar service: a small Go process