AI 资讯
Trump Administration Allows Anthropic to Release Mythos to Select US Organizations
After weeks of negotiations, the White House permitted Anthropic to restore access to its most advanced AI model for a select group of US companies and government agencies.
AI 资讯
Doctors suspected man had brain cancer. He actually had worms.
His doctors went looking for cancer, then they saw the worms' heads.
AI 资讯
I built a compiler for how AI agents should write to you
I kept correcting AI agents in the same ways: "too long," "answer first," "use a diagram," "assume I know the jargon." Each correction improved the current exchange, but the preference was not represented as durable system state. I built /calibrate-comms to make that state explicit. It is an open-source skill inside an Obsidian vault, used by both Claude Code and Codex. The model: nine operational dials The skill does not try to discover a personality type. It calibrates nine choices that directly change how an answer is rendered: Dial Practical question Density Tight sections or full reasoning? Sequence Answer first or chronological build-up? Modality Prose or diagrams for relational content? Abstraction Concrete example or principle first? Tradeoff One recommendation or several options? Detail Main path or edge cases too? Jargon Define terms or assume expertise? Tone Casual, neutral, or formal? Context-giving Should the agent extract missing context or split an overloaded brief? Prior → calibration → directives The workflow has three stages: L1 PRIOR → L2 SAMPLE REACTION → COMPILE → CLAUDE.md hypothesis empirical override shared by both agents Quick mode asks one bespoke forced-choice proxy per axis. Those questions are deliberately labelled as proxies, not validated psychometrics. Deep mode must fetch the exact items from supported open-access instruments before use; if the source cannot be obtained, the skill stays in Quick mode and makes no validation claim. The prior is then challenged through pairwise samples. For sequence, the contrast looks like this: Build-up first: We traced the latency spike to N+1 queries, then found lazy loading in a loop—so the fix is eager loading. Answer first: Fix: eager-load the association. Why: lazy loading in a loop caused N+1 queries and the latency spike. The user's pick is revealed preference. If it contradicts the prior, the sample wins. The compiler is the useful part The final profile is not a score report. A deterministi
科技前沿
Streaming services’ obnoxiously loud ads become illegal on July 1 in California
Illinois passed a similar law, giving services more incentive to make ads less booming.
AI 资讯
NYT slams Microsoft for building copyright-infringing supercomputer for OpenAI
NYT shifts OpenAI/Microsoft copyright claims after SCOTUS ruling against Sony.
科技前沿
Antibiotic "megacluster" discovery provides new strategy to fight superbugs
It's "an exciting advance in efforts to restock the antibiotic arsenal."
AI 资讯
OpenAI Has New AI Models. Here’s Why You Can’t Use Them
The White House asked OpenAI to delay the rollout of its GPT-5.6 AI models, two weeks after Anthropic had to take its most advanced AI models offline.
开发者
GitHub and UNDP team up to advance development priorities in Ghana with open source
GitHub joined the United Nations Development Programme in Ghana to explore how open source governance can support one of West Africa's most ambitious digital reform efforts. The post GitHub and UNDP team up to advance development priorities in Ghana with open source appeared first on The GitHub Blog .
AI 资讯
Vercel Introduces Eve, an Open-Source Framework for Building AI Agents
Vercel has released Eve, an open-source framework for building, deploying, and operating AI agents in production. The framework uses a filesystem-based project structure to organize agent instructions, tools, skills, subagents, communication channels, and scheduled tasks, enabling developers to define agent behavior while reducing the amount of supporting infrastructure they need to implement. By Daniel Dominguez
创业投融资
TikTok’s road to becoming a super app
TikTok may be working to become the app that people use for most of their digital activities.
创业投融资
Xprize founder says ‘humans behave better when they’re being watched’
Peter Diamandis is the latest tech executive to argue that global surveillance will make the world a better place, following Larry Ellison's comments in 2024.
科技前沿
Hollywood Thrives on ‘Rabid’ Fans. For Publicists, They’re a Nightmare
A scuffle between stan account Club Chalamet and another Heated Rivalry die-hard shines a light on how parasocial fans are a publicist’s greatest asset—and liability.
AI 资讯
Presentation: AI Works, Pull Requests Don’t: How AI Is Breaking the SDLC and What To Do About It
Michael Webster discusses the rise of headless AI agents and their impact on software delivery pipelines. He shares how massive, AI-generated pull requests create a severe bottleneck for human reviewers and introduce persistent technical debt. Learn how engineering leaders can leverage test impact analysis and automated validation pipelines to verify agentic output without sacrificing stability. By Michael Webster
AI 资讯
Dapr 1.18 Introduces Verifiable Execution, Bringing Cryptographic Trust to AI Agents and Workflows
Diagrid has announced the release of Dapr 1.18, introducing what it calls Verifiable Execution, a new set of capabilities designed to bring cryptographic trust, provenance, and tamper-evident execution records to distributed applications and AI agents. By Craig Risi
科技前沿
How Qatar Became FIFA’s Technology Test Lab
Qatar has become the place where FIFA experiments with the next generation of football technology. The results are already visible across this year’s World Cup.
开发者
Array Methods in JS - Part 2
JavaScript Array Search Methods What are Array Search Methods? Array Search Methods are used to: Find the position (index) of an element. Check whether an element exists. Retrieve an element that satisfies a condition. Find the index of an element that matches a condition. Search from the beginning or the end of an array. Common Array Search Methods Method Purpose Returns indexOf() Finds the first occurrence of a value Index or -1 lastIndexOf() Finds the last occurrence of a value Index or -1 includes() Checks whether a value exists true / false find() Finds the first matching element Element or undefined findIndex() Finds the index of the first matching element Index or -1 findLast() (ES2023) Finds the last matching element Element or undefined findLastIndex() (ES2023) Finds the last matching index Index or -1 1. Array.indexOf() Definition The indexOf() method searches an array for a specified value and returns the index of its first occurrence . If the value is not found, it returns -1 . Syntax array . indexOf ( searchElement ) array . indexOf ( searchElement , startIndex ) Parameters Parameter Description searchElement Value to search for startIndex (optional) Index where the search starts Returns Index of the first matching element. -1 if not found. Internal Working Suppose: let fruits = [ " Apple " , " Orange " , " Mango " , " Orange " ]; Memory: Index 0 → Apple 1 → Orange 2 → Mango 3 → Orange When: fruits . indexOf ( " Orange " ); JavaScript starts from index 0 : Apple ❌ Orange ✅ Found Stops immediately and returns: 1 Example let fruits = [ " Apple " , " Orange " , " Banana " ]; console . log ( fruits . indexOf ( " Orange " )); Output 1 Example - Not Found let fruits = [ " Apple " , " Orange " ]; console . log ( fruits . indexOf ( " Mango " )); Output -1 Example - Start Position let fruits = [ " Apple " , " Orange " , " Banana " , " Orange " ]; console . log ( fruits . indexOf ( " Orange " , 2 )); Output 3 Real-Time Example Suppose an e-commerce site wants to
AI 资讯
MCP Server Auth: The API Is the Real Boundary
A single shared API key is fine right up until a second person uses it. intent-brain — the system, repo qmd-team-intent-kb , renamed to the intent-brain plugin v0.4.0 this day — is a team knowledge base. A Fastify HTTP API sits over a governed memory corpus. In front of that API is an MCP server named teamkb , so a teammate doesn't open a dashboard or learn an endpoint. They ask in Claude Code and get a cited answer back with qmd:// citations. That's the whole pitch: institutional memory you query in the same place you write code. Up to this day it authenticated with one shared TEAMKB_API_KEY . The shared key has two failures that only show up once the tool has more than one user. First, every request looks identical, so the audit log can't say who asked. Second, revoking one person means rotating the key for everyone — there's no per-person handle to drop. Both are structural, not bugs you patch. You fix them by giving each person their own credential. The work closed that gap with three things, in this order: per-user tokens (identity), a server-side write gate (authorization), and a per-read access log (audit). The through-line: the API is the real boundary. The MCP client-side tool gate is UX, not security. And the per-read access log stays separate from the governance audit trail — separate log, not no log. Identity: per-user tokens replace the shared key apps/api/src/auth/token-registry.ts . Each token resolves to a record: { actor, role } , where role is 'admin' | 'member' . The shared key's two failures both dissolve here — every request now carries an actor , and revoking one person is dropping one record, not a team-wide rotation. Tokens come from layered sources, in precedence order: explicit records → a TEAMKB_TOKENS JSON env → a TEAMKB_TOKENS_FILE (default ~/.teamkb/tokens.json ) → the legacy single TEAMKB_API_KEY , which becomes one admin token with actor "shared" for back-compat. Each entry is a bearer token resolved to an identity at request time. Ma
AI 资讯
When --cap-drop ALL Broke the Gate Socket
The dogfood run went green. The gate had governed zero calls. That is the agent-governance-plane's entire job: run an AI coding agent inside a sandbox, route every tool call through a Unix-domain-socket gateway, and write a signed, hash-chained journal of every allow/deny. A green run that gated nothing isn't a pass. It's a governance plane governing air. The gate that catches its own hollowness AGP's CI dogfood doesn't just check that the harness exits 0. evidence-bundle.sh fails on a 0-gated run — if the journal shows no decisions, the build is red regardless of process exit status. That guard is what surfaced this at all: the agent process came up, the harness reported success, but the bundle had no verdicts to verify. Red. That's the last I'll say about hollow-green detection here. It's the door, not the room. The room is why zero calls reached the gate, and the answer turned out to be a collision between two things that look unrelated until you trace the syscall: Linux capabilities and a Unix socket's permission bits. The wrong theory The first hypothesis blamed the execution path. AGP has a dev-sandbox mode where the agent and the gate share a process, and a docker mode where the agent runs in a container talking to a host daemon over a bind-mounted socket. The theory was that the same-process path was short-circuiting the gate — agent and gate in one address space, the socket round-trip optimized away, decisions never journaled. Plausible. Wrong. The dev-sandbox path journaled fine in isolation. The failure only appeared in docker mode, and the moment that became clear the investigation moved from "which code path" to "what's different about the container." What's different about the container is the security posture. The real root cause: caps meet a missing write bit The short version: connecting to a Unix domain socket needs write permission on the socket file. --cap-drop ALL strips CAP_DAC_OVERRIDE — the capability that lets root ignore permission bits — s
AI 资讯
Anthropic Thinks Its Own Success Is Key to Making AI Safe
Anthropic's critics argue it's rapidly accumulating power. The company says that's what responsible AI development looks like.
AI 资讯
linux lab
Lab 1 – Linux Navigation & Files (20 points) Task 1 Create the following structure. /home/student/ ├── project │ ├── logs │ ├── scripts │ └── backup Requirements create all directories create app.log error.log install.sh inside correct folders. Task 2 Move app.log into backup Task 3 Delete error.log Task 4 Find install.sh using only one command. Lab 2 – Permissions (20 points) Create secret.txt Requirements Owner read/write Group read Others no access Verify permissions. Create a new user developer Switch to that user. Can the user read secret.txt Explain why. Lab 3 – Processes (20 points) Start a process Example sleep 500 Questions Find PID Kill the process Verify it stopped. Bonus Find the top five processes using the most memory. Lab 4 – Disk Space (20 points) Create a directory practice Generate a file about 100 MB. Questions How much disk space is used? How much free disk space remains? Which command shows directory size? Lab 5 – Networking (30 points) Find Your hostname private IP default gateway DNS server Ping 8.8.8.8 Questions Did it work? Ping google.com If this fails but 8.8.8.8 works, what is the problem? Display All listening ports. What service is listening on port 22? Lab 6 – Troubleshooting (40 points) Scenario 1 A user says "Internet is not working." Show the commands you would run. Expected ideas ip addr ping 8.8.8.8 ping google.com ip route cat /etc/resolv.conf Explain each step. Scenario 2 A website is down. Questions How do you verify server is running? nginx is running? port 80 is listening? firewall issue? logs? Commands should include systemctl status nginx journalctl -u nginx ss -tuln curl localhost systemctl restart nginx Scenario 3 SSH stopped working. How would you troubleshoot? Expected ping server ssh localhost systemctl status ssh ss -tulpn journalctl -xe systemctl restart ssh Lab 7 – Mixed Practical (Best Assessment) Tell students: You are the new Junior DevOps Engineer. The manager asks you to prepare a server. Complete all tasks. Ta