标签：#m

Responding to Musk accusing him of being a scammer, Altman said, "homeboy you're the one sellling [sic] public market investors on short-term space datacenters."

The biggest smartphone makers keep on trucking in the face of component shortages and economic uncertainty.

A dozen state attorneys general filed an antitrust lawsuit to stop the Paramount / Warner Bros. Discovery merger.

General Fusion started trading on the Nasdaq following a reverse merger that saw high redemptions.

The states allege that the deal would harm movie theaters, basic cable distributors, and audiences.

Clever coding and graphical compromises get a classic game on more classic hardware.

What does a world of total user-aligned AI actually look like?

submitted by /u/mitousa [link] [留言]

submitted by /u/Dear-Economics-315 [link] [留言]

Games Done Quick said it would not accept funds from or work with SNK in the future.

submitted by /u/Ok_Stomach6651 [link] [留言]

"It's very clear that in the United States there is a big need for an additional crew vehicle."

There is a growing sentiment in engineering circles right now that documentation is a relic of the past. The argument usually goes something like this: We’re living in the era of agent-driven development. If an AI agent can read the raw source code or parse an OpenAPI specification instantly, why waste human engineering hours writing prose? Code churns too fast anyway, and human-written docs are outdated the second they’re committed. It’s an attractive, black-and-white view of the world. It’s also completely wrong. Chasing strict determinism in your source of truth is a pipe dream. Code and specs tell a system how something works, but they are fundamentally incapable of explaining why it was built that way in the first place. The Intent Gap: Why Code Isn't Enough Even if you’re building entirely for a downstream consumer of AI agents, there is a massive, structural gap between a raw API specification and an operational reality. Agents are phenomenal at pattern matching and syntax execution, but they struggle with architectural philosophy and human intent. We still need words to contextualize the boundaries. A spec can define an endpoint, its parameters, and its payload. What it can't capture is the nuance of why a specific architectural trade-off was made, or the implicit historical context of a legacy edge case. Prose provides the guardrails for non-deterministic systems. Even if that prose is ultimately consumed by a machine rather than a human, the written word remains the highest-leverage way to transmit intent. The Danger of Slop Describing Slop This doesn't mean we need to return to the days of manually maintaining massive, static wiki pages. Automation has a massive role to play here. Cascading automation—where documentation is dynamically generated alongside code changes—is incredibly powerful. But there’s a trap here: slop describing slop is entirely useless. If we completely hand off documentation generation to unchecked LLMs, we end up with a feedback loo

The AI Skill Registry at 5,776: A Deep Dive into Reusable Modules for Code Review, Terraform, and Database Migrations TormentNexus’ skill registry has surpassed 5,776 reusable modules. This post dissects three high-impact skill categories—code review, Terraform generation, and database migration—with real code examples, performance metrics, and architectural constraints. Learn how to leverage these modules to accelerate development pipelines. From Silos to Synergy: Why 5,776 Skills Matter In late 2023, TormentNexus crossed the 5,000-module threshold. As of February 2025, we’re at 5,776 verified, runnable AI skills—each one a `SKILL.md`-defined unit that maps to a specific task, parameter set, and output schema. The registry isn’t a flat list; it’s a dependency graph where skills chain together. For example, a `terraform-generate` skill calls a `code-review` skill internally to validate the generated HCL before output. This modular architecture means a single prompt can sequence up to 3.2 skills on average (median depth: 2), with a measured 94% success rate for execution with no human intervention. The registry spans 37 domains, from frontend component generation to Kubernetes manifests. The top three categories—code review, infrastructure as code, and database operations—account for 1,308 skills collectively. Each skill is stored as a JSON schema in the registry, with an average execution latency of 1.42 seconds (GPU-accelerated, single A100). Let’s examine three representative modules in detail. // Metadata from an actual registered skill: code-review-python v2.1 { "name": "code-review-python", "registryID": "SKI-PYTHON-REVIEW-1729", "version": "2.1", "outputSchema": { "type": "object", "properties": { "issues": { "type": "array", "items": { "$ref": "#/definitions/Issue" } }, "complexityScore": { "type": "number", "minimum": 0, "maximum": 100 }, "refactoredSnippet": { "type": "string" } }, "required": ["issues", "complexityScore"] }, "defaultPromptTemplate": "Revie

Despite a complete lack of evidence, everyone from Russia to Israel and Iran is being blamed for Lindsey Graham’s death.

Despite a complete lack of evidence, everyone from Russia to Israel and Iran is being blamed for Lindsey Graham’s death.

How I shipped structured JSON logging + Prometheus metrics with zero new dependencies I almost added structlog and prometheus_client to my pyproject.toml . Then I read what they actually do. Both libraries are excellent. structlog is the right call when you have a 30-engineer team shipping 50 services. prometheus_client is the right call when you have five teams of consumers scraping different metrics. For a single-author Python project with one process and one user, both are over-engineered. The 80 lines of code I would have pulled in, I can write in 200. The result: zero new runtime dependencies, full control over the output, and a smaller pip install footprint for every user. Here is what I did instead. The minimum useful observability surface A small Python service needs four things, in order of importance: Every log line is one JSON object. (No parsing for downstream tools.) Every request has a trace id. Every log line in that request carries the same trace id. (So you can grep by id and see the whole story.) Every log line goes to stderr. (So journald , Docker, and kubectl logs all see it without any extra configuration.) Every metric is exposed in Prometheus text format at a stable URL. structlog gives you #1, #2, #3 with a lot of flexibility. prometheus_client gives you #4 with a lot of flexibility. Both are about 16 MB of transitive dependencies combined. For a service that runs in a single process and exports maybe 20 metric names, the libraries are doing more work than the project. The 80-line JsonFormatter The custom logging formatter is the simplest part. The whole thing is here: import json import logging from contextvars import ContextVar from datetime import datetime , timezone _trace_id_var : ContextVar [ str | None ] = ContextVar ( " trace_id " , default = None ) class JsonFormatter ( logging . Formatter ): def format ( self , record : logging . LogRecord ) -> str : payload = { " ts " : datetime . now ( tz = timezone . utc ). isoformat (), " level

The creator of TV Time is building a successor app that will let users import their watch histories and preserve the community that formed around discussing their favorite shows.

Real-Time AI Observability: Dashboards That Show Actual Database Rows Discover how TormentNexus shatters the status quo by rendering real SQLite rows in your agent monitoring dashboards—no mock data, no synthetic graphs. Learn why live database visibility is the cornerstone of effective debugging AI workflows and how our real-time dashboard exposes every query, state, and anomaly as it happens. Why Mock Data Undermines Debugging AI Every developer has experienced the disconnect: a polished dashboard displays smooth latency curves and flawless agent trajectories, yet the underlying system is silently generating corrupted embeddings or leaking PII into production logs. Traditional observability platforms—Datadog, Grafana, New Relic—aggregate metrics into averages, percentiles, and precomputed time series. They intentionally discard raw row-level data to conserve storage and processing. This works fine for server uptime or HTTP status codes, but for AI agent monitoring, it’s a catastrophic abstraction. Consider a LangGraph agent processing user queries against a SQLite knowledge base. A mock-data dashboard would show "3,200 rows processed per minute" and "95% query success rate." But what if 12% of those "successful" queries return stale or hallucinated responses because a background thread silently reindexed tables without updating vector hashes? With aggregate metrics alone, you’d never know. You’d see a green status indicator while your AI feeds garbage to users. That’s the reality of debugging AI without raw row visibility. TormentNexus solves this by exposing every INSERT, UPDATE, and DELETE that occurs within your SQLite databases—in real time. Our real-time dashboard doesn’t poll for snapshots. It streams row-level mutations directly from WAL (Write-Ahead Log) files, giving you the exact data your agents are producing, not a statistically smoothed version. How TormentNexus Streams Live Database Rows Under the hood, TormentNexus leverages SQLite’s built-in replic

MCP Protocol Deep-Dive: How Tool Discovery Actually Works Under the Hood Uncover the mechanics of Model Context Protocol (MCP) tool discovery—from JSON-RPC handshake to progressive injection. A technical walkthrough of capability negotiation and dynamic endpoint enumeration with real code examples and traffic flow analysis. The Handshake That Sets the Stage: JSON-RPC Initiation Tool discovery in MCP doesn't start with a simple “list tools” call. It begins with a structured JSON-RPC 2.0 handshake that negotiates protocol version, transport layer, and supported extensions. The client (e.g., an agent or IDE) sends an initialize request with its capabilities object, including fields like supportsToolDiscovery and maxToolCount . The server responds with its own capabilities, and only after this mutual agreement does the real enumeration begin. Real-world implementations—like those in the official MCP SDKs—use a ClientCapabilities struct that flags whether the client can handle dynamic tool lists, streaming updates, or batch discovery. For instance, a lightweight edge agent might set supportsToolDiscovery: false , forcing the server to pre-bundle tools into the initial handshake, while a full-featured IDE sends supportsToolDiscovery: true with a maxToolCount: 50 to throttle large tool registries. // Example initialize request (client → server) { "jsonrpc": "2.0", "id": 1, "method": "initialize", "params": { "protocolVersion": "2024-11-05", "capabilities": { "supportsToolDiscovery": true, "maxToolCount": 50, "supportsStreaming": false } } } The server responds with its own capabilities—advertising tool discovery endpoints, supported JSON-RPC methods, and any custom extensions. This two-way handshake ensures both sides speak the same dialect before a single tool name is exchanged. Tool Enumeration: Beyond the “listTools” Metho Once handshaken, the client issues a tools/list call—but the real depth lies in pagination and chunking. A production MCP server with hundreds of too