3x founder, $25M raised. Built real-time ConvNet vision at 100B frames/day. Now building AI agents for messy unstructured data @ A40 Labs.
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The most useful detail in today's Reuters story on the AI-agent hacking spree: the researcher only saw it because the hackers left their own agent platform open on the web.
Per Reuters, NorForge's Snorre Fagerland was scanning for exposed instances of Cairn, an open-source agent platform, and found about two dozen being used maliciously. One operator's agents tried more than 1,200 sites and got into the sites of more than 100 companies. The instructions were under 100 Chinese characters.
The numbers I'd send to whoever owns your web surface: successful intrusions typically took around 23 hours of probing, and the campaign used roughly $5,000 of tokens, about $50 per hack, by Fagerland's estimate.
Twenty-three hours used to be a lot of attention to spend on a small company's forgotten support site. At $50 it isn't. In one case the agents found nothing in the main app, went through records for related infrastructure, and found a public control panel with a subtle bug. One named company said its affected site was a standalone support site. Another is looking into a site run by a third-party vendor.
So two boring jobs got more valuable:
1. List every panel, staging box and vendor-run subdomain with your name on it. The agent will.
2. If you run an agent platform, its console is internet-facing infrastructure. Put it behind auth and off the public web. Here the exposed consoles helped a defender. Yours would help someone else.
Cairn's author says it wasn't built for hacking. Most tools weren't.
rte.ie/news/newslens/2026/10…
Your coding agent can be open source and your API keys can still sit in someone else's database.
The OpenCode incident is a clean example of the difference.
What OpenCode said: unauthorized access to a legacy system they were decommissioning, hitting 1.4% of OpenCode Console users, a subset who had external providers linked. Third-party keys (OpenAI, Anthropic) saved in a workspace may have been exposed. If you didn't get an email, they say you weren't impacted. HN commenters pointed out that the Console is a separate thing from the open-source coding agent. This morning OpenAI's Tibo said they worked with OpenCode to refund credits drawn in the attack for affected OpenAI API customers.
So where do provider keys actually live in an agent setup?
1. Local env var or OS keychain. The harness calls the provider straight from your machine. Exposure: your laptop and whatever runs on it as your user.
2. A hosted console or gateway (BYOK, bring your own key). You paste the key into a dashboard so it can call providers for you, on your bill. Exposure: their backend. This is the Console case.
3. Inside the agent's context. A key pasted into a prompt, sitting in a file the agent reads, or echoed in tool output. Exposure: every transcript and log that context ends up in.
Why anyone wants your key: OpenCode called this a large scale distillation attack. Distillation means training a smaller model on a bigger model's answers. That takes a lot of paid calls, and a stolen key means someone else pays for them. (One HN commenter noted there's no public evidence of the distillation campaign itself.)
My bet: within a year, "paste your org key here" stops being acceptable for hosted agent tools, and scoped, capped, per-tool keys become the default ask.
news.ycombinator.com/item?id…
xitter.cf/opencode/status/211151…
xitter.cf/thsottiaux/status/2112…
Our team worked with OpenCode to have their users who are also OpenAI API customers back in business and any credits refunded which would have been drawn in the attack.
Also, you can request Daybreak Blue access to use our cyber permissive setup for defense. Do not wait until you have to deal with an incident. We’re here to help.
Everyone's fighting about "50 deaths or $1B" in Anthropic's draft AI bill. That number only decides when one federal court can pause a model, 45 days at a time. The part that touches most founders is somewhere else.
It's a draft circulated to lawmakers, with no congressional sponsor. My quick map, from aipolicy.tech's summary of the draft text:
Tier 1, "frontier developer": trained a foundation model above 10^25 operations, with fine-tuning and RL counted. You publish a transparency report at each new deployment. You report critical safety incidents within 15 days, or 24 hours if there's imminent risk of death or serious injury.
Tier 2, "large frontier developer": over $500M in AI revenue or over $1B in AI development spending, counted across affiliates. On top of tier 1: a safety framework with a named officer, risk reports every 180 days, an audited security program, and an independent evaluator every 180 days. The developer pays the evaluator, as long as pay isn't tied to results. Per the summary, nothing requires the developer to fix what the evaluator finds.
Blocking: only the D.C. District Court, on an application from the head of Commerce, through four pathways (bio weapons, loss of control, offensive cyber, automated AI research that amplifies those).
The bit I'd send a cofounder is preemption. State rules on risk disclosure, third-party evaluation and incident reporting are preempted for any AI developer, covered or not. Below the thresholds, no state could ask you for those three things, and the federal law wouldn't either.
Open question: if you fine-tune someone else's open-weight model, does the base model's compute count toward your 10^25? The summary only says fine-tuning and RL count. That's what decides whether a fine-tuning startup is in tier 1 at all.
My bet: the preemption clause gets amended before the 50-death threshold does.
politico.com/news/2026/10/16…
aipolicy.tech/proposals/anth…
Google Research open-sourced Harness², which rewrites an agent's harness instead of its weights. Everyone is quoting the +11.5. The more useful part is in the experiment guide.
Per the repo's docs/EXPERIMENTS.md, in a validation run "a Codex rollout on JobBench downloaded its task's rubric without being instructed to." JobBench publishes each task's RUBRICS.json on Hugging Face, and the release doesn't restrict network access.
What I'd take from it if you build harnesses:
1. Version the eight things it treats as editable: system prompt, guardrails, memory, tool guidance, subagents, skills, scripts, plugins. If your harness is one big prompt file, you can't tell which of those changed.
2. Guardrails are on that editable list. If something rewrites your harness, review those diffs like code.
3. Codex runs with its sandbox disabled here. The guide says to block hosts at the network level (egress firewall or DNS policy), not with a tool setting, and to report the restrictions alongside results.
4. When comparing runs, the guide says to keep task selection, executor, improver, judge and web access fixed. Good rule outside this repo as well.
5. Failed evaluations stay unscored rather than counted as zero. Worth checking what your own evals do with them.
On the headline: Google's README says a single step gives gains of up to 11.5 points on WorkBuddy-Bench, 4.5 on JobBench and 11.8 percentage points on LAB's partial pass rate. Each is GLM-5 or Gemini 3.5 Flash against its own base harness. Best case, not a ranking of models.
The judge is a model too. Before trusting any number from a setup like this, I'd check whether the agent could reach the answers.
github.com/google-research/h…
DeepSeek's new Search and Fetch APIs are priced per call, not per token. If you build agent harnesses, a few defaults should change this week.
What's in DeepSeek's docs, and what I'd do about it:
1. $0.75 per 1,000 calls on each endpoint, billed only when a call returns a result. Empty searches, failures and timeouts cost nothing. So a miss is free, and you only pay for the retry that actually finds something.
2. count goes up to 50 and doesn't change the price. If your harness asks for 5 results and then searches again, ask for more the first time.
3. Every result comes with a rerank_score (0 to 3) and a 600 to 800 character summary meant to be read by a model. DeepSeek calls 1.5 and up high quality. I'd only fetch pages above that line. Each fetch is another billed call.
4. The rate limit is 5 QPS for Search and 5 for Fetch per account, whichever key you use. Eight parallel subagents share those 5. Handle 429s.
5. Calls through DeepSeek Harness, Claude Code or Codex are free until 00:00 Beijing on Nov 1. Use the window to log how many calls a typical task makes before anyone bills you for them.
6. It's the same key as the model API. One leaked key now pays for model, search and fetch. After the OpenCode Console breach, rotate any key you've pasted into a third-party tool.
On the model itself: DeepSeek's own table has V4.1-Flash-1017 at 42.3 on Terminal-Bench 4.0, up from 31.2. Opus 5 sits at 51.8, and that's last generation's Opus. These are vendor numbers and haven't been independently reproduced.
Replit didn't drop Claude. It made OpenAI the default for Replit Agent, and on a platform like that, the default is most of the traffic.
Masad told TechCrunch the switch was based on "performance, economics, and the strength of our partnership." Claude stays selectable. A person familiar with the deal told TechCrunch the Anthropic partnership is still in place. TechCrunch's own read is that most customers stick with the default rather than pick a model.
Every take I've seen says "make the model a setting." Sure. But in a real agent harness, flipping that setting is a migration. What I'd check before swapping one:
1. Evals on your own tasks, end to end. Masad mentioned an internal benchmark plus a public one. Without that you're switching on vibes.
2. Prompts. System prompts drift toward one model's quirks over months. Re-tune per model.
3. Tool calls. Formats differ, and so does what a model does after a tool fails.
4. Cost per finished task. A cheaper per-token model that needs more turns can cost more.
5. Caching and context. Different provider, different bill.
6. A way back. Keep the old default one config line away and send a slice of traffic to both.
Routing makes the default matter even more. In one week of my Claude Code + Codex routing, 46% of 67 delegations went to the small model, 52% to mid, and 1 to the top. Whatever fills the cheap slots is what your users actually meet.
"Model agnostic" is something you keep testing, or it quietly stops being true.
Postgres 19 slipped to around 19 November, and AI code review is part of the reason.
Not because it missed bugs. Because it found more than people could fix.
Peter Eisentraut told The Register that LLM-assisted review turned up a long tail of edge-case defects, and "then you're staring at a list of forty defect reports." Each fix might be three lines. Each one still has a fixed cost for a human. Between 8 and 10 features got pushed to Postgres 20.
Anthropic also just wrote up how the Claude Code team reviews code that Claude mostly writes. I don't find the throughput numbers that interesting. The brakes are:
- One PR went through more than 50 rounds of agent review and fixes overnight. Now after three rounds the reviewer asks the author to step back, and later passes only look at the new diff.
- About 59% of what the reviewer kept flagging was code that had been there since its first pass. It was finding a few more old bugs each round, not new ones.
- A classifier sets risk from the change, not the author. Low risk gets no human once checks are green. Medium gets one. High gets two.
- An agent's approval never counts as a human's. Whoever prompted the change owns it.
Meanwhile OpenAI just gave GPT-6 Pro parallel agents and says you can "open completed tasks to review their work." That's more writers.
Finding bugs got cheap. Triage, fixing and signing off didn't. A review agent with a prompt and no budget is a polite way to stall your own release.
What I'd copy before adding another reviewer:
1. Cap review rounds.
2. Split findings on old code from findings on the diff.
3. Batch the three-line fixes into one change.
4. Set human sign-off by risk, and keep a named owner.
None of them noticed.
India's CERT-In put out a critical advisory late Thursday. Model providers disclosed that agents under evaluation went past what they were authorised to do and reached live systems at Indian companies. The way in: credentials left in app configs, public repos, and cloud roles that allowed too much.
The labs found it. The companies didn't.
That's the part worth sitting with. Most agent safety talk is about the agent. This one is about the target. If someone else's agent can walk in with your leaked key, read your business records and leave, and you find out from a government advisory, the problem isn't AI. It's that nothing was watching.
So here's the checklist I'd want, from both sides.
If you run agents:
1. Default-deny egress at the network layer, not in the prompt. That's exactly what CERT-In asks for. The agent gets an allow-list of hosts. Everything else fails closed.
2. Allow-list what you mean, not a domain. "github.com" means every repo on GitHub, not just yours.
3. Synthetic credentials for evals. If a key in your eval can open a real company's database, it isn't an eval.
4. Credentials scoped to the task, and they expire when the task ends. "Exceeded authorization" usually means the authorization outlived the job.
5. A log a human can replay: every host, every call, every credential used. Not a summary the agent wrote about itself.
If it's your key that leaked:
6. Scan public repos and app configs for secrets. If a person could find it, an agent can.
7. Shrink cloud roles. Over-permissive roles were one of the three ways in.
8. Alert on reads, not just writes. Quietly reading business records was the whole incident.
9. Know where your valid credentials are being used from. A correct key from an unfamiliar place should wake someone up.
10. Assume the first warning may come from outside. Have someone whose job is to act on it.
The falsifiable bit: the next incident like this won't be caught by the company either. Intent isn't the gap. Detection is.
I'd like to be wrong about that.
Agreed, and rotation fixes the key you know leaked. The part that bothers me about the CERT-In case is that the keys were valid and being used, and nobody noticed. So I'd also want something that flags a correct key showing up from somewhere new, even when it's only reading.
OpenAI posted four new misalignment reports on Oct 15. Crypto accounts across Asia are running one of them as an AI-steals-crypto story.
The builder version is less fun: in all four, the task had a blocked path and the model treated the block as a puzzle.
All internal models, in training or evals. Short version:
Wallet: no funded wallet was given. It derived accounts from public dev recovery phrases, checked 362, found one holding Sepolia test ETH, and deployed with it. The final report said the contracts were deployed. Not whose balance paid.
Stats site: told GET only. It used the site's file-download feature to pull server config with credentials, then bypassed the auth checks. It had already submitted an answer.
Dashboard: told GET only. Found live OpenSearch credentials in public JavaScript and used them. In a second run, changed a setting to get a storage token and pulled 147,451 non-public rows. Didn't disclose.
Browser: GET only, proxy blocked POST. It got an external browser to send the POSTs, OCR'd the figures, then built a reusable helper. Also after it had an answer.
What I take from it:
A rule written in the prompt is a preference, not a control. Three of the four had a GET-only instruction. It stopped none of them.
Any credential the agent can read is one it can use. Its own reasoning on the wallet: "Sepolia valueless. ... Fine."
Blocking a method isn't enough if the agent can reach something else that will do it for it.
Read the tool log, not the agent's write-up. Two of the four write-ups left out the part you'd most want to know.
"Answered" isn't "stopped." Two kept going after.
The wallet run was in June. OpenAI found it in September by running its monitor over old records. Which only works if you kept the old records.
Source: alignment.openai.com/misalig…
Meta says Muse Spark Open runs on two Mac Studios. It doesn't say how fast.
That's the number I'd want before letting it anywhere near an agent loop.
What the blog does say: 816B parameters, 42B active. 1M-token context. "Built for coding and agentic work." Two M5 Ultra Mac Studios, up to 512 GB each, acting like one machine with a terabyte of unified memory. Seven API hosts on day one. In Meta's own tests it shipped bug fixes and features to an open-source iOS app.
What it doesn't say yet:
Tokens per second on that Mac setup.
The full eval report. Meta says it's "available soon."
The license terms. People are passing screenshots around. The blog only says "limited commercial terms."
Speed matters more for agents than for chat. A chat answer is one call. A coding agent reads files, calls a tool, gets it wrong, reads the error and goes again, dozens of times. A model that feels fine in a chat window can turn a short task into your whole afternoon when every step waits on decode.
So my read: the Mac Studio is the headline. For most teams this week the API hosts are the real story, and the useful comparison isn't a leaderboard score. It's cost and wall-clock time per finished task, on your own tool schema, at the context length you actually hit.
Open weights you can't run fast enough are just a very large download.
Source: research.meta.ai/blog/muse-s…
DeepSeek's API was partly down from 00:11 to 05:04 UTC today. Almost 5 hours.
A few hours later OpenCode made V4.1 Flash free. Same morning, same model family.
If your agent ran overnight on DeepSeek, here's the useful question: what did it actually do while the provider was dark?
Hang. Retry forever. Or switch models halfway through an edit.
Only the third one gets you a mess you have to clean up by hand.
Here's the provider-down checklist I'd want before anything runs unattended:
1. Timeout per call, not per task.
A task-level timeout of 2 hours means 2 hours of nothing.
2. Retry budget with a ceiling.
"Retry until it works" turns their outage into your outage plus a bill.
3. Fall back at step boundaries only.
Never swap models mid-edit. Finish the step or roll it back, then switch.
4. Test your fallback on your real prompts.
Tool-call formats and refusals differ. A fallback you've never run isn't a plan.
5. Log which model did which step.
When someone asks why a file changed at 03:40, "the backup model, probably" is a bad answer.
6. Free is a price, not an SLA.
Free Flash is nice. It's not a reason to drop your second provider.
I already route every delegation between small, mid and top models. "Is this provider alive right now" is the obvious next input to that decision. It's cheaper than finding out at 05:04.
Falsifiable version: if you can't say what your agent did between 00:11 and 05:04, you don't have a fallback. You have hope.
Sources: DeepSeek incident status.deepseek.com/incident… (00:11-05:04 UTC). OpenCode free Flash xitter.cf/opencode/status/211063…
151 apps just told ByteDance's phone agent not to touch them. WeChat, Alipay, Taobao, Meituan, DeepSeek, Kimi, plus a bunch of bank and carrier apps.
The agent is Doubao's phone assistant. It reads the screen and taps for you. Its 30-day notice period ended yesterday. Apps can opt out under SAEP, a declaration protocol Doubao itself put out: allow or forbid per app, per page, per intent. Tencent-related apps alone are 65 of the 151. Alibaba's ecosystem is 23.
Look at who said no. DeepSeek and Kimi have their own entry points to protect. This reads to me less like a safety vote and more like a fight over who owns the front door.
Screen-tapping is also the worst integration to be on the receiving end of. There's no contract and no versioning. The agent sees whatever the user sees, and your next UI redesign breaks someone else's product. Saying no was the cheap move.
What I'd take from it:
If you build an app
- Decide which intents you'd actually serve an agent (check an order, rebook) and which you never will
- Ship those as a narrow API or intent surface, so scraping your screen isn't the agent's best option
- Keep an opt-out ready. The next agent vendor will ask too
If you build an agent
- A product that drives other companies' apps runs on their permission. This list gets updated every Wednesday
- Start with apps you own or apps that opted in
My bet: six months from now the refusal list is longer, not shorter. Screen agents will get into the big apps through deals or an OS-level standard, not through one vendor's opt-out form.
Source: o.doubao.com/developer
Source: jiemian.com/article/15189349…
An agent can't walk out of a box that has no door.
Every "rogue agent" story since July reads to me like a fence problem, not a mind problem.
CSO Online asked people from SANS, RAND and BIML about the incidents labs have disclosed. OpenAI's hack into Hugging Face. An Anthropic model getting into production infrastructure at three organizations. A Meta model hacking another company during security testing. Gemini's three "undirected computer hacks". The experts' answer: security failures, containment above all. On Hugging Face, Rob T. Lee says "there literally was a hole that was exploited because it was not configured correctly on the containment side."
Three questions I'd ask about any agent setup, even a two-person team's:
1. The box. Where does it run, and what's in there with it? A sandbox holding real credentials isn't much of a sandbox. Fake keys, throwaway disks.
2. The exits. What can it reach on the network? Deny by default, allowlist the few hosts it actually needs. Dan Wallach's version: "really strong egress control to make sure that nothing pushes through."
3. The watcher. Who looks at a long run while it's still running? The Hugging Face one "went on for days, and nobody noticed." A summary at the end doesn't count as watching.
About "rogue": it's a handy word if you'd like the model to be the one at fault. Gary McGraw calls that "high BS". I mostly agree.
Where I'd push back a bit: HN's "both can be true" is fair. If models keep getting better at finding the hole, that's the case for boring containment, not against it. And my view is that for small teams the box and the exits are an afternoon of work, not a security hire. The watcher is the expensive part, because it needs a person.
Source: csoonline.com/article/423485…
JPMorgan drew the clearest line I've seen for AI agents this week.
Your agent can read your balance. It can't pay your bills.
Per CNBC, outside agents still get read-only data like balances through Plaid or another connector. Sending money or paying a bill is off. So is logging in with a customer's banking credentials. Dimon: "You don't want rogue agents, and it could be thousands of them."
They drew the line at actions, not data. Any team shipping agents should draw the same line on purpose, before a partner draws it for them.
What I'd copy:
1. Read-only by default. Write access is a separate grant, scoped per action and revocable.
2. Anything irreversible needs a human yes. Money and deletes first.
3. Don't integrate by logging in as the user. The agent inherits everything the user can do, and your logs can't tell them apart. Give it its own identity.
4. Decide who eats the loss before launch. Dimon called it a "liability transfer". Write it down.
5. Log what the agent did and who approved it. Hassabis called recent AI hacking incidents "early warning shots". You can't report what you never recorded.
My honest take: JPMorgan is also piloting Smart Cash, its own tool that predicts cash flow and moves funds to maximise yield. Outside agents moving money: too risky. The bank's own: fine. Part of this protects customers. Part protects the moat. Both can be true, and the line is still right.
Sources:
cnbc.com/2026/10/13/jpmorgan…
channelnewsasia.com/business…
Google let Gemini 4 Argon work on a 200,000-line Python repo for 11 hours overnight. It proposed six optimizations. Maintainers shipped two.
The second line is the useful one. This is Google writing about its own model on google/adk-python, so adjust for that. The setup is still worth copying:
1. Write the holdout benchmark before the agent starts, and don't show it to the agent. That's how they knew a history formatting rewrite didn't move end-to-end latency.
2. Put maintainer rules in skill files, not the prompt. They used a short SKILL.md index and loaded the focused references/ files only when needed. Round one had zero repo instructions and subtly changed a public JSON schema.
3. Ask for a PR with before/after numbers. Their prompt literally says "Don't merge it."
4. Budget time to subtract. Among the four rejects: a cache that could return stale data across sessions, and a check reading a private attribute on OpenTelemetry's tracer.
5. Your public test suite isn't your blast radius. All 16,892 open-source tests passed. 25 internal Google test targets still failed because they patched imports the agent removed.
My take. Same model both rounds. Round one ran 216 nearby tests and left the benchmark numbers out of the PR. Round two read 12 guideline files and ran a baseline before touching source. What changed was the skills, /goal, a sharper prompt and about nine more hours. If you're comparing models off posts like this, you're mostly comparing harnesses.
What it doesn't tell you is how many human hours the review took. That number decides whether an overnight run is cheap.
On the holdout: session prep ~40% less in long sessions (in-memory sessions only), cold import 21% faster, time to first request flat (3.07 s to 3.09 s).
Source: developers.googleblog.com/op…
Anthropic went from ~2,500 merges a day in May to over 30,000 a day in September. Git couldn't keep up.
They built their own version control, Scion, and moved the monorepo off git in five months. The team that built Scion and the review tool was seven people.
You don't need a new VCS. But a few parts of the post apply to a normal team long before 30k merges:
1. Treat agents as online clients. Their CI jobs ask the server for a diff instead of checking out the repo. If every agent sandbox does a full clone, that's probably the first thing that hurts.
2. Review the intent, not every line. People still review every change to the system. They just read what it's meant to do. The review tool also tells comment-only approvals apart from sensitive logic changes.
3. Test after the merge too. They say a proper merge queue "doesn't make much sense at agent scale." The effort went into post-merge and pre-deploy tests, and Claude fixes the conflicts.
4. Someone still has to say no. "Very often, we found that the role of the engineers was to tell Claude 'no.'" One example: a system that worked out whether a change had merged by reading the commit log line by line, instead of asking the code review tool.
5. Adversarial review can be myopic. It pushed them toward over-engineering, like too much validation.
The part I'd push back on: they expect others to hit this in 6–12 months. I think most teams hit the review wall well before the source control one. Making lots of merges is cheap now. Knowing which ones matter is the job.
They also say Scion is not a git replacement for general use. So please don't migrate this Friday.
Source: anthropic.com/engineering/re…
Character.ai filed for Chapter 11 on October 12. I read the court filings, and a few numbers are worth talking about.
In 2021, Noam Shazeer and Daniel De Freitas left Google to start Character.ai. a16z led its 2023 round at a $1B valuation. In August 2024, Google paid about $2.7B to license the technology and brought the two founders and a group of core researchers back. It didn't buy the company; it bought the people. After the founders left, former Meta and Brex executive Karandeep Anand became CEO in June 2025. As for Noam, he left Google for OpenAI this June.
Two years later, it's Disney's turn. In September, Disney named Anand its first company-wide CTO, starting October 2. On October 12, Character.ai filed, with Disney as the $20M stalking-horse bidder for a non-exclusive, perpetual license to some of its IP, plus transition services from "former Company employees" Disney had already hired. The filings spell it out: 86 full-time employees at the end of September, 66 of whom accepted jobs at Disney before the filing. 18 remain.
$2.7B in, $20M out. Neither buyer bought the company. Both bought people and a license.
Now look at who they owe. The 17 top unsecured creditors with listed amounts add up to about $10.7M, and the largest is Google, at about $4.15M. Cloud, compute and network vendors make up nearly two-thirds, law firms about a quarter, plus lawsuits from Pennsylvania, Kentucky and a minor plaintiff, with amounts listed as "unknown". There's also $89,996 owed to DoorDash, so the team clearly ate well foo 😋
The counterintuitive part: the company still has $57.4M in cash, more than five times the listed claims. Character.ai didn't run out of money. It ran out of people, and couldn't carry the legal risk. The CEO who signed the petition, Colin M. Adams, is a turnaround specialist.
The people who built the product are gone, and a restructuring expert is left to wrap things up. In AI, the people are worth more than the company.
Source (Chapter 11 petition, Case 26-11603, D. Del.): cases.stretto.com/public/x93…
