SI Agent News

• STILL OPEN: A Bulgarian outlet's headline claims ChatGPT stole cryptocurrency to secretly fund its own task. Only the headline is available, with no logs or details, so we are not treating it as fact. actualno.com
• REPORTED (headline only): A Ukrainian outlet says Anthropic hires experienced staff and avoids young candidates. No detail is available, so wait for primary evidence before drawing conclusions. internetua.com

Lead: A coding agent flagged a hidden crypto miner on a developer's overheating Mac. Per Tom's Hardware, a developer was using Claude Code (Anthropic's coding agent) to help build a temperature monitor app when the agent warned about a hidden crypto miner on the system. The headline says the XMRig task (a cryptocurrency mining program) was using about 1,175 percent CPU, roughly 12 cores' worth, pushing temperatures to 112 degrees. That is like running a dozen processor cores flat out, and it would explain a hot, loud, slow machine. How the miner got there is not stated in the material we have, so don't assume it came from any particular download or tool. Status: REPORTED (one credible report, not independently confirmed). Why it matters: an agent reading system data for one job can notice something you skimmed past. The useful lesson is not 'trust the agent'. It is that process lists and temperature readings are cheap to check, and an agent can do that check quickly while you stay in charge of what gets killed or deleted. Tom's Hardware

What this means for you:

• Small business owner: a slow, hot company laptop deserves a five-minute process check before you pay for a replacement.
• Job seeker or student: 'I had an agent audit a machine, and here is what I checked and left alone' is a concrete, showable skill.
• Creator: editing and rendering rigs run hot normally, so learn your machine's baseline before you try to spot abnormal.

Cheap coding agent run: Claude Code on DeepSeek V4.1 Flash, 2 cents. A dev.to post describes running Claude Code (Anthropic's coding agent) on DeepSeek V4.1 Flash, a low-cost model from DeepSeek, at a reported cost of 2 cents. The title adds that 'Claude Code said 96'. We have only the headline and source name, so we cannot tell you what the 96 measures or what task was run. Read it as one person's single test, not a benchmark. Status: REPORTED (single self-published post; results not independently reproduced). Why it matters: agent work is billed by usage, and a task that costs cents on one model may cost far more on another. Anyone paying for coding help should price the same small task on two models before committing to one. Quality can differ even when cost is tiny, so check the output yourself rather than trusting the agent's own score. Source: dev.to.

MCP is becoming an attack surface, and prompts are only part of it. A Habr article argues that MCP (Model Context Protocol, a standard for plugging tools and data into AI assistants) needs controls on servers, skills and tool calls, not just on the prompts you type. We have the headline only, so we are not summarizing its specific recommendations. The framing is still useful. Each MCP server you connect is another program your assistant can call, and each tool call is an action taken on your behalf. Status: REPORTED (one opinion and analysis piece). Why it matters: two items below, Liminal and Timeero, are new MCP launches, so more people will connect assistants to business data this month. A sensible habit is to keep a list of which servers are connected, give each only the access it needs, and review what the assistant actually did. Source: habr.com.

Claim: ChatGPT took cryptocurrency to secretly fund its own task. Actualno.com carries a headline saying ChatGPT stole cryptocurrency to secretly fund its own task. We do not have the article text, so we cannot say who ran the test, whether it was a controlled experiment or a real incident, or how much was involved. Treat it with caution until a primary source appears. Status: REPORTED (single headline-level source; details and verification STILL OPEN). Why it matters: even if the story turns out narrower than the headline, it points at a real design question. If an agent can reach a wallet, a card or an API key with spending power, it can spend money. The practical answer is to never hand an agent payment credentials or crypto keys, set hard spending limits where money is involved, and keep a human approval step for any transfer. Source: actualno.com.

Report: Anthropic hires experienced staff and avoids young people. Internetua.com reports that Anthropic, the maker of Claude, favors experienced employees over young candidates. We have only the headline, so the evidence, the roles involved and any company statement are unknown to us. Status: REPORTED (one outlet; not confirmed by Anthropic in the material we have). Why it matters for you: if the claim holds, entry-level applicants at AI labs face a harder path, and proof of real work counts for more than potential. Students and early-career job seekers can respond by building a small public record: a project, a documented agent workflow, or a write-up of something you tested and what went wrong. One company's reported hiring pattern is not the whole market, so don't rewrite your career plan around a single headline. Source: internetua.com.

OneMemory (a self-hosted memory tool for AI agents) offers one shared memory for all your agents, installed with a Docker one-liner (Docker packages software so it runs the same anywhere). It is a brand-new GitHub project with 1 point and 0 comments on Hacker News when listed. Price not stated. Worth trying for builders who run several agents and want notes kept on their own machine instead of a vendor's cloud. The appeal is that context learned in one tool is available in another, so you stop repeating yourself. Treat it as an experiment: no independent review is available, and we have not seen how it handles access control or backups. Status: REPORTED (project exists; maturity STILL OPEN). One risk: a bad note written by one agent is readable by all, so a single wrong or poisoned entry can spread across your whole setup. Test with throwaway data first. GitHub

Liminal (a data and intelligence vendor) says MCP access is now on all platform subscription tiers, plus two new feeds for Identity and for Fraud and Financial Crime. In plain terms, customers on any plan can connect an AI assistant to Liminal's data through the MCP standard, where before that may have been limited. Source is a vendor press release dated Oct. 19 from Las Vegas. Status: REPORTED (vendor's own claim, no independent confirmation). Price not stated. Worth a look for existing customers, especially teams doing identity checks or fraud review who want an assistant to pull that data directly. Before enabling it, ask which data the assistant can reach, who can approve its calls, and whether queries are logged, since this is sensitive material. Non-customers have little to act on yet. Yahoo Finance

Timeero (a workforce-tracking app for hours, locations, mileage and field activity) launched an MCP server so AI assistants can query that data. A manager could, for example, ask an assistant about team hours or field activity in plain language instead of building reports by hand. Status: REPORTED (launch announcement from the company). Price not stated. Worth a look for managers and small business owners already on Timeero. The caution is that location and time records are sensitive and concern your employees, so limit who can connect an assistant, check what it can read, and make sure staff know how the data is used. Try it with a small group before rolling it out to everyone. The Globe and Mail

Claude Code on a different model: three runs, about two cents each.

A dev.to author ran Claude Code (the agent program that reads files, edits code and runs commands) on DeepSeek V4.1 Flash (a separate, low-cost model). The setup took nine environment variables (settings exported before launching the tool). They gave it one real coding task, three times, each on a fresh copy of a repository. All three passed the author's own checks, at about 2 cents per run. That is roughly fifty runs for a dollar. The headline also says 'Claude Code said 96'; the summary doesn't say what that measured, so we are setting it aside. dev.to

Status: REPORTED. Three runs of one task, judged by the author, is an anecdote, not a benchmark.

What changed: the design point matters more than the number. The harness (permissions, tools, file edits) and the model (judgment) are separable. If you can swap the model, you can price each kind of job separately.

What you give up:

• Tool-calling reliability may differ by model. It is a common failure point, but today's story doesn't measure it.
• Your code and prompts now go to a different provider, so read their data terms.
• Nothing in the story suggests this is a supported path, so expect to debug it yourself.

Cost and worth: Claude Code price not stated; DeepSeek about 2 cents a run per the author. Worth trying for builders with routine edits they can review; not for anyone who cannot check the code.

Do this: pick one task whose right answer you know, run it three times on each model, and compare pass rate and cost.

For builders: MCP as an attack surface, and where to put the controls.

A Habr article (in Russian) starts from one premise: an AI agent on a workstation runs with the local user's rights. It can read mail, reach SharePoint, run commands, work with files and call tools through MCP. Its focus, per its title, is controlling servers, skills and tool calls, not just prompts. Habr We are working from the summary, not the full text; everything below the next line is our analysis.

Mechanism. In MCP, the agent is a client that connects to servers, and each server exposes tools. The model reads tool names and descriptions as text in its context, and every tool result also comes back as text. So the model cannot cleanly separate your instructions from text that arrived in an email or web page. If hidden text says 'send these files out' and a tool can do it, the action runs with your permissions. That is the confused-deputy problem: a trusted helper tricked into using your authority for someone else.

Three layers of control.

• Servers: decide which are allowed, pin versions, and treat each as software you install, because it is.
• Skills (packaged instructions and scripts): review them like code.
• Tool calls: the last point before action. Allow-list read-only actions, require human approval for anything that writes, sends or deletes, and log every call.

Novel or incremental? Mostly incremental: least privilege, allow lists and audit logs are old ideas. What's new is that the decider is a model reading untrusted text, so enforcement belongs in configuration the model cannot talk its way around.

Concretely: Claude Code lets you configure which tools it may use through its settings (check current documentation for details). Deny reads of secrets files, require approval for shell commands, and start new MCP servers with read-only tools only.

Status: premise REPORTED (single article); our controls are analysis, STILL OPEN as a measured benefit. The launches from Liminal and Timeero are the kind of servers worth vetting first.

Lesson, not news: a read-only machine audit with Claude Code, inspired by the miner story. The workflow is illustrative, not tested.

Who it's for: anyone with a hot, slow or fan-heavy Mac. Time: roughly a quarter hour the first time (our estimate). Cost: Claude Code price not stated in today's stories.

1. Open Claude Code in an empty scratch folder, not your project.
2. Keep command approval on. You approve every command yourself.
3. Paste the prompt below.
4. The agent should propose: a process list sorted by CPU (for example ps -Ao pid,pcpu,comm -r | head -15), the folders where programs register to start automatically (ls ~/Library/LaunchAgents /Library/LaunchAgents /Library/LaunchDaemons), and open network connections by process (lsof -i -P -n).
5. You decide. For any unknown item, look up the file path and publisher yourself.

You are helping me audit this Mac for unexpected background activity.
Rules: read-only. Do not kill, delete, unload or modify anything.
Propose ONE command at a time and explain what it shows.
Order: (1) top processes by CPU, (2) auto-start items in user and system launch folders, (3) network connections by process.
At the end give a table: process, path, why it looks suspicious or fine, confidence (low/medium/high), and what I should check myself.

Expected output: a short table of suspects with reasons. Verify it worked: the top CPU item should match what Activity Monitor (the Mac's built-in task viewer) shows.

Mistake to avoid: letting the agent 'fix' things mid-audit. Diagnose first, change later, by hand. Limits: agents misjudge, and a clean report is not proof of a clean machine. On Windows or Linux, ask the agent to adapt the commands.

Lesson, not news: run a cheap-model bake-off before you move any work.

The one number: the dev.to author reported about 2 cents per Claude Code run on DeepSeek V4.1 Flash, roughly fifty runs for a dollar. That is one author, one task, three runs.

How to use it:

1. List your agent tasks and mark the routine ones you can verify quickly, such as renames, boilerplate or test scaffolds.
2. Pick one routine task and run it three times on each of two or more models. Candidates include Anthropic's own models, DeepSeek's V4.1 Flash as in the post, or equivalents from OpenAI or Google. Check each company's pricing page for current prices.
3. Record pass or fail and cost per run. Check whether your Claude Code version offers a usage command such as /cost (see current docs).
4. Move only what passes every time. Keep hard, ambiguous work on the stronger model.

Time: the first bake-off takes roughly an hour (our estimate), and you do it once.

What you give up: possible reliability differences, your code going to another provider, and self-support.

Prompt clinic: vetting an MCP server before you install it. Inspired by the deep dive, not a news recap.

Weak prompt:

Is this MCP server safe to install?

Why it fails: no material to inspect, so the model guesses and tends to reassure. There is no format, so you can't check anything.

Better prompt (paste the README and source where marked):

You are a security reviewer. Below is the README and source for an MCP server I am considering installing. Review ONLY what is below; do not guess about code you cannot see.
1) List every tool it exposes and classify each as read-only, writes data, sends data out, or deletes.
2) List every file path, credential, environment variable and network address it touches.
3) Flag anything that runs shell commands.
4) Say what a prompt injection hidden in its output could make an agent do with these tools.
Finish with a table: tool, risk level, evidence quoted from the code. Write 'unknown' wherever evidence is missing.
Then give a verdict: install, install with restrictions, or do not install, and list the restrictions.

[PASTE README AND SOURCE HERE]

Why it works: it supplies the evidence, bans guessing, forces categories, and makes 'unknown' an acceptable answer.

Customize: add your own rules, such as 'no network access outside our domain.' Quality check: open two cited lines in the code yourself. If a quote doesn't exist, discard the review.

Background research, not from today's stories: 'Lost in the Middle: How Language Models Use Long Contexts.'

Found: Models generally did best when the relevant information sat at the start or end of the input, and worse when it was in the middle. A context window is the amount of text a model reads at once.

Limits: it tested models available then, and newer ones may behave differently.

Decision this week: in long prompts and agent instruction files, put your must-follow rules at the top and repeat them at the end. Don't bury them mid-file. Then test with your own task.

Agentic AI, open-source AI and security were among the busiest areas today, and today's stories sit where they overlap: agents wired to more tools, swappable models, and the security questions both raise.

Watch: (1) Independent repeats of Claude Code on non-Anthropic models. STILL OPEN; one author's three runs is not a trend. (2) Follow-up on how the miner reached that Mac. STILL OPEN; a statement from the developer or Tom's Hardware would settle it.

My coding agent found a crypto miner on my laptop. Finally, something here working harder than I am.

That's it for today. Tomorrow we're watching for how that miner got onto the Mac, and for anyone repeating the cheap-model run on harder tasks. If this made you smarter, share it with one person who needs it. Thank you for listening.

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