Donald Trump’s base has been increasingly spilt. Laura Loomer’s sudden embrace of Ukraine has only widened that divide.
While most gaming headsets have moved towards low-latency wireless connections, there’s something to be said for a budget-friendly, capable wired headset. The EPOS H3 Hybrid is on sale for just $24.99 at Woot, a whopping 75 percent off its original price of $99.99. It has an unorthodox batch of features: USB-A and 3.5mm options for […]
Make a plan before you use the new Apple Upgrade program to avoid potential fees down the road.
In June, Nintendo announced a new version of the Switch 2 that should let you easily replace its battery pack - but only for Europe. Logitech will do much the same. User-replaceable batteries will be the law in Europe in February 2027, but instead of bringing them to the rest of the world, Logitech plans […]
Ozlo’s first major update to its sleep earbuds introduces longer battery life, improved connectivity, enhanced audio, and new sleep features as the startup continues the product line once abandoned by Bose.
According to NASA, two of the three reaction wheels that control the spacecraft's alignment have failed, and there are problems with one of the spacecraft's thruster systems.
Rep. Kevin Mullin (D-California ) has proposed a bill that would direct federal regulators to establish minimum national safety standards for autonomous vehicle operators.
Hey Hacker News! I built Writekin over the past week because I was tired of AI writing that didn't sound like me, even though I had just used AI to clean it up, rather than wholesale write it. The usual fixes I found online for this were: - Some sort of SKILL.md, or - A system prompt full of rules to strip the generic AI tells (e.g. no em-dashes, none of the stock phrases, varying the sentence length, etc). While those cleaned up the surface a bit, Pangram still came back as ~100% AI written, wh
Instead of immediately opening pull requests when newer dependency versions are released, Dependabot now waits three days before suggesting upgrades, thus increasing the likelihood that malicious releases are identified and removed before they can be integrated. By Sergio De Simone
Foreman is an agentic coder that runs as Kubernetes resources. You describe work as a Workload, it decomposes into tasks, agents running on your nodes pick them up, and a branch comes out the other end with something deterministic standing between that branch and your main. This is the walkthrough. Four objects to understand, an install, an agent, a verifier, and a real run. Every command and every output below is from a working cluster. The four objects Foreman is deliberately small. Almost everything you do is one of these. Agent is a worker definition: which model it talks to, which tools it may call, and what budget it gets. An Agent has a role , and the two that matter here are coder and verifier . Workload is the unit of work you actually author. It carries an intent, a repository, and which agents to use. AgenticTask is what a Workload decomposes into. You rarely write one by hand; you read them to see what is happening. FleetNode is a node that has advertised itself as able to run tasks. The scheduler matches a task's required capabilities against these. The shape of a run is: you apply a Workload, the controller synthesizes AgenticTasks, the scheduler routes each to a FleetNode whose agent can serve it, the agent runs the model in a loop with tools, and the result lands as a branch plus a verdict. The idea underneath it Worth stating plainly, because it shapes every design decision: the model is not trusted, and specifically its claim to have succeeded is not trusted. A coder agent finishes by calling a tool that says "I am done, verdict GO." Foreman treats that as a request, not a result. If the model says GO and produced no diff, the run is recorded as NO-GO. If the verifier's checks do not pass, the work does not land, no matter how confident the summary was. That is the difference between an agent that writes code and a system you can leave running. Everything else in this post is plumbing around that idea. Install Foreman ships as a Helm chart that dep
This is the architecture of Shibui Finance , an MCP server that gives Claude direct SQL access to 64 years of US stock market data. About 10,000 symbols, 31 million daily price records, quarterly financials back to 1990, 56 pre-computed technical indicators, and 6.4 million SEC filing records. Free to use. Stack: Python, PostgreSQL, dbt, DuckDB, FastMCP, Caddy. Runs on a single VPS. Data pipeline Three stages: ingest into PostgreSQL, transform with dbt, export to DuckDB. Data APIs / SEC EDGAR / FRED | Python ETL (Polars, ADBC) | PostgreSQL clean_* schemas (~50 raw tables) | dbt (27 models) staging -> integration schema (17 analytical tables) | DuckDB export (daily, ~14 GB file) | FastMCP server (read-only, streamable-http) | Caddy (TLS) -> mcp.shibui.finance Multiple sources feed the pipeline: commercial data APIs for prices, fundamentals, valuations, and estimates. SEC EDGAR for filing metadata and insider transactions (bulk historical + a 5-minute Atom feed for near-real-time). FRED for FX rates to normalize non-USD fundamentals. Public registries for ticker classification. The ETL is a Python CLI organized by data source. Each module has its own fetcher, loader, and CLI. A single all command runs everything in fixed sequence. You can't refresh 10,000 tickers daily without hitting rate limits, so the ETL rotates: each run refreshes the stalest 5% of tickers. Full universe cycles in about 20 runs. Recent prices always refresh on every run. Every table write is a single transaction. DROP + CREATE inside a transaction, rollback on failure. The database never serves partial data, and dbt always sees complete tables even when ingest jobs overlap. The dbt layer 27 models in two tiers. The process layer handles standardization: enriching symbols with security types and exchange mappings, linking SEC amendment filings to their originals, repairing filer date typos. The integration layer produces the 17 tables that Claude actually queries. This is where raw normalized tabl
This article provides a step-by-step guide to building and testing a cross-cloud currency agent. A coordinator built with Strands Agents and hosted on Amazon Bedrock AgentCore Runtime (in AWS us-east-1 ) discovers and delegates to a Google ADK agent (on GCP Cloud Run in us-central1 ) over A2A v1.0 , cross-checks results against an MCP exchange-rate tool , and measures what independent cross-cloud verification costs in latency, reliability, and overhead. What is This Project Trying to Do? Most Agent-to-Agent (A2A) protocol demos stop at "look, the HTTP 200 OK request succeeded." That is a smoke test, not an interoperability benchmark. This project goes further: an Amazon Bedrock AgentCore-hosted Strands Agents coordinator discovers and delegates to a Google ADK agent running on GCP Cloud Run, comparing the results against a local MCP stdio exchange-rate tool backed by live Frankfurter daily reference rates. We also compare the performance, developer experience, and wire compatibility directly against our previous benchmark run hosted on Microsoft Foundry in Azure ( gpt-5-mini ), giving us a true cross-cloud benchmark across AWS, Azure, and GCP. The questions we answer with hard empirical data rather than vibes: Can an AgentCore-hosted Strands agent discover and invoke a Google ADK agent through an A2A agent card with no framework-specific glue? What latency and token overhead does remote-agent verification add? Does independently verifying an MCP tool result over A2A improve correctness or failure recovery enough to justify that overhead? How does AWS Bedrock AgentCore Runtime compare like-for-like with Microsoft Foundry on Azure? Reduce, Re-Use, Re-Cycle! This builds directly on the currency agent from the previous articles in this series: Getting Started with MCP, ADK and A2A | Google Codelabs GitHub - jackwotherspoon/currency-agent That agent — built with Google ADK, Gemini 2.5 Flash, and a FastMCP exchange-rate server backed by the free Frankfurter API — serves a
The CW's live sports programming is coming to ESPN on August 4.