标签：#ia

The unexpected interruption came hours before the president wrote a congratulatory post on Truth Social about the company’s most recent earnings report.

Fraudsters are exploiting enterprise chat apps like Teams and Webex to trick Chinese victims into transferring large sums of money, fueling a wave of complaints.

There's a lot of capital pouring into the business of giving models away.

"The public square is open again, and definitely not affiliated with X in any way."

Sophia Bendz, general partner at Cherry Ventures, stopped by Equity to break down the latest in the Swedish tech ecosystem.

Modders are trying out an unofficial version of Nvidia's DLSS 5 on Skyrim, Cyberpunk 2077, GTA V, and a bunch of other games after code for the AI upscaling tech appeared in an early-access build of NBA 2K27. Members of the RenoDX modding channel on Discord reportedly found a way to extract the DLSS "Neural […]

Retro, a friend-focused photo-sharing app built by former Instagram employees, has raised more than $21 million in Series A funding.

A recent paper argues that AI is often better at doctoring than doctors. Guess who isn't thrilled.

Now, Apple TV subscriptions will cost $14.99 per month, up from its previous price of $12.99 per month.

If an AI assistant can write code but cannot reliably hash a value, inspect a JWT, validate JSON, or calculate a CIDR range, you have a small but recurring reliability problem. Asking the model to do those jobs from memory adds an unnecessary interpretation step. DevUtils MCP Server packages 36 everyday developer utilities behind the Model Context Protocol . The server runs locally over standard input and output, so an MCP-compatible client can call explicit tools instead of guessing an operation. This tutorial connects the released 1.1.0 package, verifies the protocol handshake, and shows how to choose a useful tool without treating the server as a replacement for application libraries. TL;DR Install Node.js 18 or newer, add the server command to your MCP client's configuration, restart the client, and ask it to use a tool such as json_validate , jwt_validate , or cidr_calculate . The smallest configuration is a command plus the package name: { "mcpServers" : { "devutils" : { "command" : "npx" , "args" : [ "devutils-mcp-server" ] } } } The released package declares Node.js >=18 . The repository's current default branch has moved ahead to 1.1.1 , so the commands and behavior in this article target the immutable v1.1.0 release and the npm latest package that was verified during research. Prerequisites You need: Node.js 18 or newer and npm. An MCP-compatible client that supports a local stdio server. Permission to run npx and download the public npm package on first use. No API key, account, database, or external service is needed for the local server. The MIT-licensed repository lists Claude Desktop, Cursor, VS Code, Windsurf, Docker, and other MCP-compatible clients as possible consumers. Their configuration file locations differ, but the server entry is the same. Install the released server The release README documents an npx path that does not require a global installation: npx devutils-mcp-server For an automated setup where accepting the package prompt must be e

Sandhya Devanathan will oversee some OpenAI operations across Southeast Asia and Australia in her new role.

The art portfolio platform Cara, designed for creators who don’t want their work used to train AI, has been under assault by trolls seizing and publishing its data.

The company is testing robots that can swap cables, reset servers, and take on other tasks performed by technicians, fueling concerns among some workers that their jobs could be at risk.

A complete guide to the NestJS request lifecycle: the exact order middleware, guards, interceptors, pipes, and filters run, and why it matters.

You’ve probably used Gemini to analyze hours of video, summarize podcasts, or answer questions from...

Designing Systems That Contain Failure — CS Week Perú 2026 On August 13, 2026, I had the opportunity to speak at CS Week Perú 2026 , an event organized by IEEE Computer Society student chapters across Peru. My session was: “Isolation and Trust Boundaries in Production: Designing Systems That Contain Failure” The talk explored how production systems can be designed to limit the impact of failures through explicit trust boundaries, architectural invariants, and evidence-based validation. The central idea was simple: The goal isn't to prevent every failure. The goal is to control its blast radius. Production systems fail. Requests overlap, processes crash, memory is exhausted, credentials can be compromised, and dependencies can become unavailable. Reliable engineering is not about assuming that none of these things will happen. It is about deciding what can be affected when they do . From Unit Tests to System Properties A green unit-test suite demonstrates that the tested units behave correctly under the conditions we defined. But it does not necessarily demonstrate that the system as a whole preserves its architectural properties under concurrency, multiple tenants, resource exhaustion, or real deployment conditions. A function can be correct in isolation while the system still violates an important invariant. That led to one of the central questions of the talk: What properties must never be violated? Trust Boundaries I used the concept of a Trust Boundary to make architectural assumptions explicit. For each boundary, we can ask three questions: What are we protecting? What is allowed to cross the boundary? What happens if the condition is violated? From there, we can define invariants : properties that the system must preserve under the conditions established by its design. In the architecture discussed during the session, three dimensions were particularly important: Context → Logical isolation Identity → Cryptographic isolation Execution → Physical/process isolat

Free Tokens Are Not an SLO: An Ops Cost Drill for AI Batch Queues This week, two numbers trended: a harness at 100%, a model at 30%. For platform teams, a better pair is queue age and deadline slack. This article is a cost drill for the simplest AI batch path: free tokens, free server, non-negotiable deadline. Disclosure: This article was prepared as part of MonkeyCode's product outreach. MonkeyCode offers free model access and a free server option. That capacity is real. It is not an SLO. The tokens cost nothing. The queue is patient. Your deadline is not. The missing variable Token cost is easy to measure. Operations cost is easy to ignore. A free endpoint converts a per-token bill into a per-hour bill. The bill becomes your time, your retries, and your queue age. This drill keeps the ledger honest. It answers one question: what does a completed request cost when the token price is zero? Topology # worker.py (minimal, single-threaded) import queue import time import csv work = queue . Queue () for i in range ( 1000 ): work . put ({ " id " : i , " prompt_tokens " : 512 , " max_tokens " : 256 }) def call_model ( payload ): # replace with your free model endpoint return { " ok " : True , " in_tokens " : 512 , " out_tokens " : 180 } completed = 0 retries = 0 started_at = time . time () while not work . empty (): item = work . get () attempt = 0 while attempt < 4 : try : call_model ( item ) completed += 1 break except Exception : retries += 1 attempt += 1 time . sleep ( 2 ** attempt ) The worker is deliberately single-threaded. Free capacity often serializes. Serialization turns a token problem into a time problem. Declared test conditions 1,000 requests. One worker process. One free model endpoint. No client-side rate limiting. Deadline: 30 minutes. Ledger: one CSV row per request. Ledger and report # cost_ledger.py import csv import time HOURLY_OPS_COST = 50.0 # loaded engineering rate, adjust def record ( item , elapsed , retries ): with open ( " ledger.csv " , " a

A boutique StrictlyVC evening returns to New York's West Village on September 10 with Keith Rabois, Craig Shapiro, Jason Levien, Tristan Walker, Brynn Putnam, and Deven Parekh — covering AI, sports investing, community-building, venture economics, and politics, with cocktails, food, and networking throughout the night.

Your filesystem is already a database. Most tools just don't treat it that way. That's the core idea behind the Markdown Database Pattern — written up properly on The Way of Markdown , a site we've been contributing to that makes the case for building things on plain markdown instead of locked-in platforms. We think it's a pattern worth more attention, so here's the short version. Treat a folder of markdown files as a database. Each file is a record. Frontmatter fields are columns. Directories are tables. Tags, wikilinks, and tasks in the body become queryable relations. Filesystem Database ────────────────────────────────── markdown file → record frontmatter field → column directory → table #tag → tag relation [[wikilink]] → link relation - [ ] task → task relation You get portability, version control (git works perfectly on plain text), no framework lock-in, and full queryability. You give up scale and real relational joins — this isn't for millions of records. It's a lightweight database, honest about its limits. Once you name it, you start seeing it everywhere. Obsidian Bases and Dataview already do versions of this, half-consciously. A team wiki where every page has a status and owner field is one. A blog with date and tags in frontmatter is one — it just doesn't know it yet. Sweet spot: up to roughly 10k files. Past that, reach for a real database. Below it, this gets you almost everything a database gives you, at a fraction of the complexity, with none of the lock-in. The full writeup — the complete tradeoff analysis, a worked example with actual queries, how to implement it in a weekend, and the tool ( MarkdownDB ) that does it for you — is here: wayofmarkdown.com/markdown-database

The setting will prevent your posts from being recommended in the algorithmic "discover" feed.