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Hey DEV, I'm Tobore. Let's actually connect. I've been on here for a while now, mostly writing and...

The company didn't immediately say what went wrong. SpaceX's stock plunged more than 4% in after-hours trading before paring losses.

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On today’s Uncanny Valley, we unpack OpenAI’s ongoing drama, both legal and reputational, and whether these developments could further hurt the company—particularly in its fight against Anthropic.

From data preparation and tokenizer selection to pretraining, LoRA, RLHF, evaluation, and production monitoring, this guide covers the major stages involved in training an AI model. Training an artificial intelligence model is not simply a matter of loading a dataset onto a GPU and running a few commands. A successful model requires a measurable objective, legally usable and carefully cleaned data, an architecture suited to the problem, controlled optimization, independent evaluation, and continuous monitoring after deployment. In large language model development, a mistake in any one of these stages can waste millions of training examples and a significant amount of compute. This guide explains the model development process primarily through the training of large language models. However, fundamental concepts such as dataset splitting, loss functions, overfitting, and evaluation also apply to computer vision, speech, and predictive models. The goal is not to provide a single fixed recipe. Instead, it is to explain which training approach is appropriate for which problem and to clarify the cost difference between training a model from scratch and adapting an existing model. In Brief: How Is an AI Model Trained? First, the target task and success criteria are defined. Data is collected, reviewed for licensing and privacy, cleaned, and divided into training, validation, and test sets. The model generates predictions from the input data. The difference between the prediction and the correct target is measured using a loss function. Backpropagation calculates how each parameter contributed to the error, and an optimization algorithm updates the parameters. This process is repeated under controlled conditions until the model achieves acceptable results in independent tests and safety evaluations. What Does Training a Model Actually Mean? A neural network initially contains a large number of numerical parameters. During training, the model generates a prediction for a giv

Foot traffic to leafy green chains is falling, data shows. Still, a few brave souls who spoke to WIRED were determined to get their fix. “I honestly didn’t even think about” the risk of explosive diarrhea, one says.

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One of the best things about the Laravel AI SDK is that it doesn't lock you into a provider. Swap OpenAI for Anthropic, add Gemini as a failover, and your agent code barely changes. That's the entire pitch, and it mostly holds up. Until your agent needs to browse the web. The provider-agnostic promise has a hole in it The SDK ships two provider tools for giving agents access to the web: WebSearch and WebFetch . They're convenient, a couple lines and your agent can search or fetch a page. But they're not really part of the SDK's unified layer, they're implemented natively by whichever AI provider you're using, which means their availability depends entirely on which model you picked. WebSearch works on Anthropic, OpenAI, Gemini, and OpenRouter. WebFetch only works on Anthropic and Gemini. So if you configure a failover chain that includes Groq, DeepSeek, Mistral, or xAI, and one of those becomes the active provider, your agent quietly loses the ability to browse. Not an error, not a warning, just an agent that stops citing sources or stops noticing the page it was supposed to check even changed. That's a rough thing to discover in production. You built a failover chain specifically so a rate limit or outage wouldn't take your feature down, and it turns out one of your fallback providers was silently missing a capability the whole time. Even when it works, it's pretty shallow Set the provider problem aside for a second. Even on a provider where WebSearch and WebFetch are both available, what you get back is raw: search results or fetched page content that the model has to reason over itself. There's no schema. There's no guaranteed citation tracking. If you want your agent to return a structured, sourced answer, that reasoning happens somewhere in the model's own inference, not in a layer you control or can test. For a lot of use cases, that's fine. For anything where the answer needs to be defensible or reproducible, a research assistant, a competitive brief, anythin

The Quest Begins (The "Why") I still remember the first time I walked out of a coding interview feeling like I’d just lost a lightsaber duel. The problem was a simple array‑rotation task, but I dove straight into typing, eyes glued to the screen, and barely said a word. When the interviewer asked, “What are you thinking right now?” I froze, mumbled something about “just trying to get it done,” and watched the seconds tick away. The feedback later? “Great coding skills, but we couldn’t follow your thought process.” That moment stung because I knew I could solve the problem—I just hadn’t learned how to show my thinking. After a few more silent attempts, I realized the interview isn’t a solo boss fight; it’s a co‑op mission where the interviewer wants to see how you navigate the terrain. If they can’t hear your internal monologue, they have no way to gauge your problem‑solving instincts, communication style, or ability to catch mistakes early. I went on a quest for a repeatable, low‑effort way to narrate my thinking without turning the interview into a monologue. What I found was a three‑step verbal framework that felt like unlocking a new Force power—simple, repeatable, and surprisingly effective. The Revelation (The Insight) The technique I now swear by is State → Plan → Execute . At each stage you say out loud exactly what you’re doing, using a tight, repeatable script. It’s not about over‑explaining; it’s about giving the interviewer a clear map of your mind. Here’s the exact wording I use, broken down by phase: State – Clarify the problem, assumptions, and constraints. “Okay, so we need to rotate an array to the right by k steps. I’m assuming k can be larger than the array length, so I’ll use modulo to normalize it. The array can contain any integers, and we should aim for O(n) time and O(1) extra space.” Plan – Outline the high‑level approach before writing a line of code. “My plan is to use the three‑step reversal algorithm: reverse the whole array, then reverse

Most remote job listings are ghosts — already filled, never opened, or posted just to farm résumés. As a developer, that annoyed me enough to solve it with code instead of complaining about it on X . So I built Remoty.work , which grades every listing A–F on how likely it is to be real . Here's how the detection actually works under the hood. The problem, from an engineering angle Job boards are an endless scrape loop. Board A scrapes Board B, which scraped Board C. The same dead listing propagates across a dozen sites, and none of them verify anything. There's no signal for "is this real," so the noise compounds until every board looks identical. I didn't want to build board number thirteen in that loop. I wanted a layer on top that answers one question every listing should have to: is anyone actually going to read my application? The architecture Everything runs on a single VPS — Postgres, scrapers, and the scoring jobs, supervised on a schedule. Deliberately boring. High level: Ingestion: scheduled scrapers pull from source boards and company ATS feeds into Postgres. Each raw listing is deduplicated by a fingerprint (title + company + normalized URL) so the same job reposted across five boards collapses into one row with a repost_count . Scoring engine: every listing gets a ghost-risk score from a handful of signals, then mapped to an A–F grade so it's human-readable, not a black-box number. The "rant" signal: I cross-reference what people say about companies in places like r/recruitinghell and hiring threads. That's where the truth about a company's hiring leaks out, and it turns out to be a strong predictor. Agents: I use DeepSeek to classify and summarize the messy text job descriptions, company chatter. DeepSeek because running this over thousands of listings every night on GPT-4-class models would have killed the unit economics before I had a single user. One infra detail I didn't expect to spend a weekend on: the frontend is on Cloudflare's edge, but the ed

The company endorsed landmark AI transparency laws in California and New York last year, but its head of US state and local policy says they may already be outdated.

Metron was the Greek word for a measure: the standard you judge a thing against. It gives us metric, and metronome. Pick the wrong metron and everything you count against it comes out wrong, no matter how carefully you count. Ask most teams how they know AI adoption is working, and the answer is some version of: we’re shipping more. More PRs, more tickets closed, more velocity on the board. None of those numbers were ever measuring what people thought they were measuring. They were proxies, and bad ones, for whether the team was creating value. AI didn’t break that. It just made the proxies lie louder. An agent can produce PRs faster than any team can review them. It can close tickets all day. None of that tells you whether the thing built was worth building, or whether it fixed the actual problem, or whether anyone downstream is better off. Burning tokens isn’t the same thing as creating value. It just looks the same on a dashboard built to reward the first thing. The same DORA numbers I pointed at earlier are blunt about it: individual output climbs while delivery throughput and stability drop. Same bottleneck, counted at the wrong end . This is Theory of Constraints in one sentence: speeding up a step that wasn’t the bottleneck doesn’t speed up the system. It just piles more work in front of whatever the real bottleneck is. Implementation used to be the constraint. Now it isn’t. Review is. Coordination is. Making sure everyone agrees on what “done” even means is. Agents made the fast part faster and left the slow part exactly where it was, except now it’s buried under more output arriving to be reviewed by fewer people who understand any of it. Measuring individual speed after that shift is measuring the wrong clock. The team can be faster and worse off in the same quarter. The instinct, when this gets noticed, is to add another skill to the agent. Automate the review step too. Automate the coordination. Whatever’s slow, throw a capability at it. That doesn’t fix

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When the enemy occupies favorable terrain, don't attack head-on. Let them think they're safe, let...

OnePlus promises to continue supporting the phones it has already released.