Voice translations preserve speaker's tone, pacing, pitch—with SynthID watermarks for security.

From the press release: Today we’re launching Claude Fable 5 : a Mythos-class 1 model that we’ve made safe for general use. Fable 5’s capabilities exceed those of any model we’ve ever made generally available. It is state-of-the-art on nearly all tested benchmarks of AI capability, showing exceptional performance in software engineering, knowledge work, vision, scientific research, and many other areas. The longer and more complex the task, the larger Fable 5’s lead over our other models. Releasing a model this capable comes with risks. Without safeguards, Fable 5’s capabilities in areas like cybersecurity could be misused to cause serious damage. We’ve therefore launched the model with safeguards that mean queries on some topics will instead receive a response from our next-most-capable model, Claude Opus 4.8. To release the model both safely and quickly, we’ve tuned these safeguards conservatively—they’ll sometimes catch harmless requests, though they trigger, on average, in less than 5% of sessions. With more capable models arriving in the coming months, we’re working to improve our safeguards and reduce false positives as quickly as we can. For a small group of cyberdefenders and infrastructure providers, we’re also launching Claude Mythos 5 . It’s the same underlying model as Fable 5, but with the safeguards lifted in some areas. 2 Mythos 5 will initially be deployed through Project Glasswing, in collaboration with the US government, as an upgrade to Claude Mythos Preview. It has the strongest cybersecurity capabilities of any model in the world. Soon, we intend to expand access to Mythos 5 through a broader trusted access program. submitted by /u/alphacolony21 [link] [留言]

If those same AI workloads can be handled by cheaper models without affecting quality, it would mean a massive shift in the economics of AI.

This sounds crazy but it's actually real... These guys from AI Love Jazz are running a music contest, and the top song will be performed on stage by real musicians. What's your take on that? Have you seen anything like this before? Feels like the moment AI is finally blending with the music industry - and it's not as hated as you'd think. I composed songs with Suno AI myself and happy to see such initatives. submitted by /u/Double-Ad-4640 [link] [留言]

Almost two years ago there was a thread on this (https://news.ycombinator.com/item?id=40872102). I'm curious now that more time has passed what people think?

Good news for Claude devs deploying on Google Cloud. Claude Fable 5 is now in General Availability (GA) on Google Cloud. You can now access Fable 5 , as well as other Anthropic models - including Claude Opus 4.8 and Claude Sonnet 4.6 - on Agent Platform . Read more here -> [ blog ] Happy building!

submitted by /u/cnn [link] [留言]

"Artemis III will be an extraordinary demonstration of what is possible."

Shareable blog post edition: https://andymaleh.blogspot.com/2026/06/andys-laws-of-ai-in-software-engineering.html Law #1: "The more Software Developers use AI, the more valuable Software Engineers who do not use AI become." Software Engineers who are masters at delivering Software without using AI will actually have increased job security the more Software Developers in the worldwide Software Development community rely on AI to deliver Software without having true mastery over Software Engineering. As more Software Developers become fully dependent on AI to build Software without truly understanding how AI gets work done, Software Engineers who do understand what is going on under the hood will dwindle and become more valuable than ever. In other words, they will have a competitive advantage over Software Developers who can only deliver Software features with AI as well as Software Developers who have not mastered Software Engineering. Also, there will always be a need for Software Engineers who can maintain the Software of AI itself. Law #2: "Software Developers benefit from AI in direct proportion to how weak they are in Software Engineering" The weaker Software Developers are at Software Engineering the more they benefit from AI. After all, AI learns from Master Software Engineers and then applies its learnings in code generation done for lower-level Software Developers who lack mastery in Software Engineering. So, users of AI simply place themselves lower in the expertise hierarchy to be on the receiving end of what Master Software Engineers feed AI with their code. This explains why many experts like Linus Torvalds do not find AI very useful while devs who have zero degrees and qualifications feel like they get a lot from AI. A beneficial thing to learn from this law is that it is more valuable for a Software Developer to hone in their Software Engineering skills (including the completion of university degrees) than to hone in their AI usage skills because if t

SQL is arguably the most widely used language in software engineering, yet it is often the least carefully written. Most teams enforce strict linting on their application code but leave SQL queries as a free-for-all. This guide covers the formatting rules that separate maintainable, team-friendly SQL from query spaghetti that haunts on-call rotations. Why Poorly Written SQL Is a Real Engineering Problem Unformatted SQL is not just an aesthetic issue - it is a correctness risk. Dense, run-on queries make it nearly impossible to spot accidental Cartesian products, missing GROUP BY clauses, or WHERE conditions that silently bypass indexes. By the time a performance problem surfaces in production, tracing it back to the root cause becomes a painful exercise in reading someone else's stream of consciousness. Rule 1: Keyword Capitalization SQL engines treat select and SELECT identically, but human readers do not. Always uppercase reserved keywords such as SELECT, FROM, WHERE, JOIN, GROUP BY, and ORDER BY. Keep table names, column names, and aliases lowercase. This single habit immediately creates a visual boundary between the logic structure of the query and the underlying data it operates on. Rule 2: Indentation and Clause Alignment Think of SQL clauses as layers in a data pipeline. Each major clause - SELECT, FROM, WHERE, GROUP BY, ORDER BY - should start at the left margin. Columns and filter conditions beneath them should be indented by 4 spaces (or 1 tab, as long as your team is consistent). This structure lets any reviewer skim the query top-to-bottom and understand the data flow at a glance. Rule 3: Trailing vs. Leading Commas This is a genuinely debated topic on data teams. Leading commas (placing the comma at the start of each new line) make version control diffs significantly cleaner when columns are added or removed. Trailing commas look more natural for developers coming from JavaScript or Python. Neither approach is wrong - what is wrong is mixing both styles

When you expose inventory or checkout endpoints to public-facing front-ends or third-party webhooks, safeguarding those APIs from brute-force scripts, scraping bots, and inventory hoarding algorithms becomes a critical requirement. Without defensive rate limiting, a single coordinated script can easily overwhelm your database connections. The Problem with Simple Counter Resets A common mistake when setting up basic API protection is using a rigid "Fixed Window" counter (e.g., allowing 100 requests per minute, resetting exactly at the turn of the clock). This creates a massive flaw where a developer can flood your server with 100 requests at 11:59:59 and another 100 requests at 12:00:01, effectively doubling your acceptable burst traffic and causing severe performance dips. To handle uneven burst traffic safely without crashing your database, the standard approach is implementing a token bucket algorithm. The Token Bucket Pattern The token bucket algorithm maintains a centralized bucket that holds a maximum capacity of tokens. Tokens are added back to the bucket at a constant, predictable rate over time. Each incoming API request consumes exactly one token. If the bucket is completely empty, the request is instantly rejected with a 429 Too Many Requests status code, protecting your core server threads. javascript // Quick Redis-based token bucket rate limiter concept async function isRateLimited(userId) { const key = `rate:${userId}`; const now = Date.now(); // Use a Redis multi-exec transaction to atomically check and update tokens const [tokens, lastRefill] = await redis.hmget(key, 'tokens', 'lastRefill'); // Calculate token replenishment based on time elapsed... // Return true if tokens <= 0, otherwise decrement tokens and update timestamp }

Exactly one year ago, my terminal was a blank slate. I started where almost everyone does , wrestling with HTML, CSS, and JavaScript, trying to understand the web pixel by pixel. What began as curiosity quickly turned into a full obsession. I went from building simple static pages to diving deep into full-stack development. Here’s what that intense first year of constant building, breaking things, and shipping real projects has looked like. The Leap into Modern Frameworks Once vanilla JavaScript started feeling limiting, I jumped into React . Component-based thinking completely changed how I approached interfaces. Then came Next.js — it bridged client-side beauty with server-side power and pushed me into a true full-stack mindset. The Ultimate Test: Lynvista Safaris The biggest challenge was building lynvistasafaris.com , a live travel booking platform for a real client. This project forced me far beyond tutorials and into real engineering problems. I had to implement: Payment infrastructure from scratch with Daraja API (M-Pesa STK pushes) and Paystack for international transactions. A robust database layer using Drizzle ORM + MySQL. Custom business logic , including a multi-currency pricing system with special validation rules for currencies like GBP. It was messy , many late nights debugging webhooks, schema mismatches, and edge cases , but shipping it taught me more than any course ever could. What One Year Taught Me Syntax is just a tool. The real skill is learning how to break down complex problems, debug effectively, and keep iterating even when things break in production. I’m incredibly proud of the progress I’ve made in 365 days (402 contributions later), but I know I’m still at the very beginning. For the next phase, I’m focused on shipping more projects, writing about the crazy bugs I encounter, and deepening my architectural and full-stack skills. If you want to see what I’m building right now, check out my GitHub .

In Q1 2026, OpenAI and Anthropic moved enterprise customers from flat-rate plans to token-based billing. The change looks administrative, but it had a direct consequence for engineering teams: the real cost of AI became visible for the first time. The market's reaction over the following two months was enough to reopen a question many considered settled: does AI actually deliver measurable ROI? What happened when the bill arrived The most documented case is Uber. The company had encouraged all employees to use agentic tools as much as possible and even ranked AI usage internally on leaderboards. The result: the entire annual budget was consumed in four months. The response was a $1,500/month cap per employee per agentic coding tool (Claude Code, Cursor, and similar). At Brex, engineers were limited to $500/week in tokens; employees outside engineering received a $5/week cap. T-Mobile temporarily capped usage at $2,000/month per user with plans to migrate to a tiered system. One unnamed company, according to Ed Zitron in "AI Is Slowing Down" (June 2026), spent $500 million on Anthropic models in a single month due to absent spend controls. These are not isolated cases. A KPMG survey reported by the Wall Street Journal in June 2026 found that only 26% of companies have a comprehensive view of their AI costs; 50% have partial visibility; and 22% only find out what they owe after the bill arrives. Steve Chase, KPMG's global head of AI, told the Journal: "It's a new resource that needs to be managed that didn't exist quite that way, and we're seeing exponential growth." The structural problem behind the spending caps The spending caps are a symptom. The root cause, as Zitron details in the same article, is that the economics of generative AI require numbers that currently seem out of reach. Anthropics has made over $330 billion in compute commitments with Google, Amazon, and Microsoft, plus another $45 billion with CoreWeave and SpaceX. To cover those commitments, it nee