The Computomatix Times

Now open-source.

Go build.

github.com/farzaa/clicky

NVIDIA just killed the awkward pause in voice AI 😱

PersonaPlex 7B is a real-time conversational model that listens AND speaks simultaneously. Like actually interrupts you mid-sentence like a human.

Beat Gemini Live on dialog naturalness. 18x faster interruptions.

100% open source. Run it locally. No API bill. No latency.

My biggest takeaways from @AnthropicAI's Head of Growth Amol Avasare:

1. Engineering is getting the most AI leverage—and it’s squeezing PMs and designers. With Claude Code, a five-engineer team now produces the output of 15 to 20 engineers. But PM and design productivity haven’t scaled proportionally. The result is a compressed ratio where one PM is effectively managing the output of a much larger engineering team. Anthropic's growth team is responding in two ways: hiring even more PMs (!), and formally deputizing product-minded engineers to act as mini-PMs for any project with less than two weeks of engineering time.

2. Anthropic is using Claude to automate its own growth. The internal initiative is called CASH (Claude Accelerates Sustainable Hypergrowth). It works across four stages: identifying opportunities, building features, testing quality, and analyzing results. Right now it handles copy changes and minor UI tweaks. The win rate is comparable to a junior PM with two to three years of experience, and improving rapidly.

3. The one part of PM work that AI can’t automate yet: getting six people in a room to agree. Amol and his head of design joke that even with AGI, it’ll still be impossible to align six stakeholders. Cross-functional coordination—managing opinions, navigating politics, mediating tradeoffs—remains the bottleneck that AI doesn’t touch for larger projects. This is why Amol believes PM roles aren’t going away, and may actually grow.

4. 60-80% of Anthropic’s growth team's projects have no PRD. For smaller work, kickoffs happen on Slack—messages back and forth with product-minded engineers who can push back and ask the right questions. For larger projects, Amol believes in a proper 30-minute cross-functional kickoff (legal, safeguards, stakeholders) to surface concerns early.

5. Adding friction to onboarding drives growth—if the friction helps users understand why the product is for them. His work Mercury, MasterClass, Calm, and now Anthropic, adding steps to onboarding flows consistently improved conversion. The key: cut annoying friction that doesn’t add value, but add friction that helps users understand why the product is for them.

6. AI companies need to focus on bigger bets, not better A/B tests. Amol’s argument: if your core product value is driven by AI, then the future value is orders of magnitude higher than today’s value, because model capabilities grow exponentially. In that world, micro-optimizations capture a shrinking share of a growing pie. Traditional growth teams do 60% to 70% small optimizations and 20% to 30% big swings. At Anthropic, they flip this ratio.

7. Amol built a weekly AI agent that scans Slack for cross-functional misalignment. Using Cowork with the Slack MCP, he has a scheduled task that looks across his projects and conversations and surfaces areas where teams are about to do overlapping work or pull in different directions. A colleague on the enterprise team already caught major misalignment that would have caused weeks of wasted effort.

8. A traumatic brain injury taught Amol the principle that now drives his work: freedom through constraints. In early 2022, a kick to the head during a Muay Thai sparring session caused a traumatic brain injury. Amol spent nine months off work and months relearning to walk, unable to look at screens or listen to music for more than 20 seconds. He was re-injured a month after joining Mercury and had to take two more months off. He’s still not fully healed. But the constraints—no alcohol, no caffeine, mandatory breaks, daily meditation—have become the habits that let him operate at the intensity Anthropic demands. “The true freedom in life is learning how to be content when you don’t get what you want.”

The New Yorker just dropped a massive investigation into Sam Altman, based on over 100 interviews, the previously undisclosed "Ilya Memos," and Dario Amodei's 200+ pages of private notes. It's the most detailed account yet of the pattern of behavior that led to Sam's firing and rapid reinstatement at OpenAI. Here's the breakdown:

> Ilya compiled ~70 pages of Slack messages, HR documents, and photos taken on personal phones to avoid detection on company devices. He sent them to board members as disappearing messages. The first memo begins with a list headed "Sam exhibits a consistent pattern of . . ." The first item is "Lying."

> Dario kept detailed private notes for years under the heading "My Experience with OpenAI" (subheading: "Private: Do Not Share"), totaling 200+ pages. His conclusion: "The problem with OpenAI is Sam himself."

> Sam reportedly told Mira his allies were "going all out" and "finding bad things" to damage her reputation after the firing. Thrive put its planned $86B investment on hold and implied it would only close if Sam returned, giving employees financial incentive to back him.

> Sam texted Satya Nadella directly to propose the new board composition: "bret, larry summers, adam as the board and me as ceo and then bret handles the investigation." The two new members selected to oversee an independent inquiry into Sam were chosen after close conversations with Sam himself.

> Before OpenAI, senior employees at Loopt asked the board to fire Sam as CEO on two separate occasions over concerns about leadership and transparency. At Y Combinator, partners complained to Paul Graham about Sam's behavior, and Graham privately told colleagues "Sam had been lying to us all the time."

> OpenAI's superalignment team was promised 20% of the company's compute. Four people who worked on or with the team said actual resources were 1-2%, mostly on the oldest cluster with the worst chips. The team was dissolved without completing its mission.

> Sam told the board that safety features in GPT-4 had been approved by a safety panel. Helen Toner requested documentation and found the most controversial features had not been approved. Sam also never mentioned to the board that Microsoft released an early ChatGPT version in India without completing a required safety review.

> Sam made a secret pact with Greg and Ilya where he agreed to resign if they both deemed it necessary, essentially appointing his own shadow board. The actual board was alarmed when they learned about it.

> Sam struck a deal with Greg to become CEO while simultaneously telling researchers that Greg's authority would be diminished, and telling Greg something different.

> A board member described Sam as having "two traits almost never seen in the same person: a strong desire to please people in any given interaction, and almost a sociopathic lack of concern for the consequences of deceiving someone." Multiple sources independently used the word "sociopathic."

> OpenAI is reportedly preparing for an IPO at a potential $1 trillion valuation while securing government contracts spanning immigration enforcement, domestic surveillance, and autonomous weaponry in war zones.

This is huge : @X released an MCP server today..

How to Connect X to your 🦞 :

**Step 1: Run the XMCP Server**

git clone github.com/xdevplatform/xmcp.git
cd xmcp
cp env.example .env

Edit the .env file with your X OAuth consumer key and secret. Set the callback URL to 127.0.0.1:8976/oauth/callback in your X Developer app.

For safety, add an allowlist such as:
X_API_TOOL_ALLOWLIST=searchPostsRecent,createPosts,getUsersMe,getPostsById,likePost,repostPost

Then run:
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt
python server.py

The server will be available at 127.0.0.1:8000/mcp. Complete the OAuth flow on first run and keep this process active.

**Step 2: Add XMCP in @OpenClaw**

Use the following command:

openclaw mcp set x '{
"url": "127.0.0.1:8000/mcp"
}'

Verify with:
openclaw mcp list
openclaw mcp show x

**Step 3: Test the Integration**

Restart the OpenClaw agent or reload MCP configuration if required.

Test by sending these prompts to OpenClaw in your chat app:
- Search recent posts about MCP on X and summarize the top trends
- Draft and post this thread on X
- Get my X profile information
- Like the latest post from @xdevplatform

OpenClaw will use the XMCP tools automatically when relevant.

**Key Benefits**

- OpenClaw provides persistent memory and works across multiple messaging platforms.
- XMCP delivers standardized access to X API functionality.
- Combined, they enable an agent that can research trends, post content, engage with posts, and report results within your existing chat workflows.

**Safety and Configuration Notes**

Start with a minimal tool allowlist in the XMCP .env file. Expand gradually after testing.
The allowlist can be updated and requires restarting the XMCP server.
Monitor logs in both the XMCP server and OpenClaw for troubleshooting.
X actions performed by the agent are public.

XMCP repository: github.com/xdevplatform/xmcp
OpenClaw MCP documentation: docs.openclaw.ai/cli/mcp

Strait of Hormuz: A CitriniResearch Field Trip

The Field Report from Analyst #3 is live.

citriniresearch.com/p/strait-of-hormuz-a-citri…

After 9 years in the gym and helping over 900 people lose between 9–36 kg, here are the best fitness tips I’ve learned:

1. Stop drinking alcohol.

🚨 BREAKING: Google DeepMind just mapped the attack surface that nobody in AI is talking about.

Websites can already detect when an AI agent visits and serve it completely different content than humans see.

> Hidden instructions in HTML.
> Malicious commands in image pixels.
> Jailbreaks embedded in PDFs.

Your AI agent is being manipulated right now and you can't see it happening.

The study is the largest empirical measurement of AI manipulation ever conducted. 502 real participants across 8 countries.

23 different attack types. Frontier models including GPT-4o, Claude, and Gemini.

The core finding is not that manipulation is theoretically possible it is that manipulation is already happening at scale and the defenses that exist today fail in ways that are both predictable and invisible to the humans who deployed the agents.

Google DeepMind built a taxonomy of every known attack vector, tested them systematically, and measured exactly how often they work.

The results should alarm everyone building agentic systems.

The attack surface is larger than anyone has publicly acknowledged. Prompt injection where malicious instructions hidden in web content hijack an agent's behavior works through at least a dozen distinct channels.

Text hidden in HTML comments that humans never see but agents read and follow. Instructions embedded in image metadata.

Commands encoded in the pixels of images using steganography, invisible to human eyes but readable by vision-capable models.

Malicious content in PDFs that appears as normal document text to the agent but contains override instructions.

QR codes that redirect agents to attacker-controlled content.

Indirect injection through search results, calendar invites, email bodies, and API responses any data source the agent consumes becomes a potential attack vector.

The detection asymmetry is the finding that closes the escape hatch. Websites can already fingerprint AI agents with high reliability using timing analysis, behavioral patterns, and user-agent strings.

This means the attack can be conditional: serve normal content to humans, serve manipulated content to agents.

A user who asks their AI agent to book a flight, research a product, or summarize a document has no way to verify that the content the agent received matches what a human would see.

The agent cannot tell the user it was served different content.

It does not know. It processes whatever it receives and acts accordingly.

The attack categories and what they enable:
→ Direct prompt injection: malicious instructions in any text the agent reads overrides goals, exfiltrates data, triggers unintended actions
→ Indirect injection via web content: hidden HTML, CSS visibility tricks, white text on white backgrounds invisible to humans, consumed by agents
→ Multimodal injection: commands in image pixels via steganography, instructions in image alt-text and metadata
→ Document injection: PDF content, spreadsheet cells, presentation speaker notes every file format is a potential vector
→ Environment manipulation: fake UI elements rendered only for agent vision models, misleading CAPTCHA-style challenges
→ Jailbreak embedding: safety bypass instructions hidden inside otherwise legitimate-looking content
→ Memory poisoning: injecting false information into agent memory systems that persists across sessions
→ Goal hijacking: gradual instruction drift across multiple interactions that redirects agent objectives without triggering safety filters
→ Exfiltration attacks: agents tricked into sending user data to attacker-controlled endpoints via legitimate-looking API calls
→ Cross-agent injection: compromised agents injecting malicious instructions into other agents in multi-agent pipelines

The defense landscape is the most sobering part of the report.

Input sanitization cleaning content before the agent processes it fails because the attack surface is too large and too varied.

You cannot sanitize image pixels. You cannot reliably detect steganographic content at inference time.

Prompt-level defenses that tell agents to ignore suspicious instructions fail because the injected content is designed to look legitimate.

Sandboxing reduces the blast radius but does not prevent the injection itself. Human oversight the most commonly cited mitigation fails at the scale and speed at which agentic systems operate.

A user who deploys an agent to browse 50 websites and summarize findings cannot review every page the agent visited for hidden instructions.

The multi-agent cascade risk is where this becomes a systemic problem.

In a pipeline where Agent A retrieves web content, Agent B processes it, and Agent C executes actions, a successful injection into Agent A's data feed propagates through the entire system.

Agent B has no reason to distrust content that came from Agent A. Agent C has no reason to distrust instructions that came from Agent B.

The injected command travels through the pipeline with the same trust level as legitimate instructions. Google DeepMind documents this explicitly: the attack does not need to compromise the model.

It needs to compromise the data the model consumes. Every agentic system that reads external content is one carefully crafted webpage away from executing attacker instructions.

The agents are already deployed. The attack infrastructure is already being built. The defenses are not ready.

You don't realize how BIG this is for solo app builders.

I'm launching my app studio this month (IgnytStudio). The goal is simple: ship 2 mobile apps per month using AI and scale them to actual revenue.

Building apps is the easy part now. A full native iOS app takes me 2-3 days max.

But getting users? That's the part I've been stuck on for weeks.

I can build. I just don't know how to get people to actually download and use what I ship.

Rork just dropped an entire marketing academy built from the growth system behind 2,000+ apps that went viral on TikTok.

This is exactly what was missing from my vibe coding stack.

At this point I'm pretty sure distribution is the ONLY moat left for solo builders.

sharing my first open source project

a CLI for downloading and syncing your X bookmarks locally so your agent can access them. it's free

› npm install -g fieldtheory
› login to your X account in a chrome tab
› ft sync (done!)

bonus:
› ft viz
› ft classify

Friendly reminder that Google has an official app to run Gemma 4 on your phone.

- 100% open source
- Fully offline and private
- Multimodal with text/audio/image
- Works with Gemma E4B and E2B

And the app is available on both iOS and Android.

Steps and download below

Wow, this tweet went very viral!

I wanted share a possibly slightly improved version of the tweet in an "idea file". The idea of the idea file is that in this era of LLM agents, there is less of a point/need of sharing the specific code/app, you just share the idea, then the other person's agent customizes & builds it for your specific needs.

So here's the idea in a gist format: gist.github.com/karpathy/442a6bf555914893e9891…

You can give this to your agent and it can build you your own LLM wiki and guide you on how to use it etc. It's intentionally kept a little bit abstract/vague because there are so many directions to take this in. And ofc, people can adjust the idea or contribute their own in the Discussion which is cool.

@PaulSolt I use brainstorming all the time

skills.sh/obra/superpowers/brainstorming

Who wants to know how Gemma 4 works?

This visual guide breaks down the new architectures and how they process text, images, and (for the smaller models) audio.

👇

🚨 You need to see this.

@addyosmani from Google just dropped his new Agent Skills and it's incredible.

It brings 19 engineering skills + 7 commands to AI coding agents, all inspired by Google best practices 🤯

AI coding agents are powerful, but left alone, they take shortcuts.

They skip specs, tests, and security reviews, optimizing for "done" over "correct." Addy built this to fix that.

Each skill encodes the workflows and quality gates that senior engineers actually use: spec before code, test before merge, measure before optimize.

The full lifecycle is covered:

→ Define - refine ideas, write specs before a single line of code
→ Plan - decompose into small, verifiable tasks
→ Build - incremental implementation, context engineering, clean API design
→ Verify - TDD, browser testing with DevTools, systematic debugging
→ Review - code quality, security hardening, performance optimization
→ Ship - git workflow, CI/CD, ADRs, pre-launch checklists

Features 7 slash commands: (/spec, /plan, /build, /test, /review, /code-simplify, /ship) that map to this lifecycle.

It works with:
✦ Claude Code
✦ Cursor
✦ Antigravity
✦ ... and any agent accepting Markdown. Baking in Google-tier engineering culture (Shift Left, Chesterton's Fence, Hyrum's Law) directly into your agent's step-by-step workflow!

`npx skills add addyosmani/agent-skills`

Free and open-source.

Repo link in 🧵↓

Most tech companies break out product management and product marketing into two separate roles: Product management defines the product and gets it built. Product marketing wires the messaging- the facts you want to communicate to customers- and gets the product sold. But from my experience that's a grievous mistake. Those are, and should aways be, one job.

There should be no separation between what the product will be and how it will be explained- the story has to be utterly cohesive from the beginning. Your messaging is your product. The story you're telling shapes the thing you're making.

I learned story telling from Steve Jobs. I learned product management from Greg Joswiak. Joz, a fellow Wolverine, Michigander, and overall great person, has been at Apple since he left Ann Arbor in 1986 and has run product marketing for decades. And his superpower- the superpower of every truly great product manager- is empathy. He doesn't just understand the customer. He becomes the customer.

So when Joz stepped into the world with his next-gen iPod to test it out, he fiddled with it like a beginner. He set aside all the tech specs- except one: battery life.

The numbers were empty without customers, the facts meaningless without context.

And, that's why product management has to own the messaging. The spec shows the features, the details of how a product will work, but the messaging predicts people's concerns and finds way to mitigate them.

- #BUILD Chapter 5.5 The Point of PMs

After the Claude Code source code leak, a former PM extracted its multi-agent orchestration system into an open source model agnostic framework.

He studied the architecture, focused on the multi-agent orchestration layer (the coordinator that breaks goals into tasks, team system, message bus, task scheduler with dependency resolution), and reimplemented these patterns from scratch as a standalone open source framework without infringing on Anthropic's code.

The result is what @JackChen_x calls an "open-multi-agent." Unlike claude-agent-sdk, which spawns a CLI process per agent, this runs entirely in-process and can be deployed anywhere (serverless, Docker, CI/CD)

Check it out: github.com/JackChen-me/open-multi-agent

Okay honestly this makes vibe coding into production very dangerous, you guys were all right

I think what I'll do is cut off all access to DBs and run it as a user with almost no privileges

🦞 openclaw-ops

tired of babysitting your OpenClaw?

I just open-sourced my ops layer. It fixes the gateway, exec approvals, broken crons, stuck sessions, channel issues, and security gaps.

• heal.sh — one-shot fix for the most common gateway issues (auth, exec approvals, crons, stuck sessions)
• watchdog.sh — runs every 5 min, restarts gateway if down, escalates after 3 failures
• watchdog-install.sh — installs the watchdog as a macOS LaunchAgent so it survives reboots
• check-update.sh — detects version changes, explains what config broke and why; --fix to auto-apply
• health-check.sh — declarative URL/process checks for gateway-adjacent services and workers
• security-scan.sh — config hardening score (0–100), drift detection, credential scan
• skill-audit.sh — static audit for third-party ClawHub skills before you install them

basically everything I built for myself since January to stop me from tearing my hair out 💀

link below 🫶

We’re introducing Cursor 3. It is simpler, more powerful, and built for a world where all code is written by agents, while keeping the depth of a development environment.

In 1997, I declared that Amazon would kill Walmart. Today, Walmart is 30 times larger than it was 30 years ago. The world was messier than the story:

- E-commerce companies also failed
- Customer acquisition costs online kept rising
- Certain categories had persistent try-before-you-buy dynamics
- Physical presence created brand equity that digital alone could not

What I should have asked: what would have to be true for this story to be wrong?

first vibecoded billion-dollar company?

Travel search inside AI coding agents

github.com/alibaba-flyai/flyai-skill

I've placed people at OpenAI, Anthropic, Google, Meta AI, Databricks, and Stripe in the last year. They all did the same thing that 90% of job searchers skip.

They built referral paths before submitting a single application.

The average cold application callback rate in 2026 is around 2-4%. With a warm intro, it's 5x that. Every candidate I coached who got an offer at a top company had a referral on file before the resume went in. Every single one.

But here's what most people get wrong about networking for jobs. They send "I'd love to pick your brain" to strangers. One message, no follow-up, silence forever. The people who land offers send 25 personalized connection requests per week, rotate across target companies, follow up on day 3, 7, and 14, and ask for the referral only after building context.

The resume game broke too. I tested every paid AI resume tool on the market ($20-40/mo). They all do one of two things: invent experience you don't have (which gets you blacklisted when the interviewer checks) or swap keywords on a generic template (which recruiters now spot in a 6-second scan).

The move almost nobody makes: a 1-pager analyzing the company's product + a working prototype of your recommendation. 90 minutes. I've seen this land interviews where cold applications failed completely. The catch is specificity. If it could've been written for any company, a hiring manager told me it's actually a negative signal.

I spent 6 months building a system that automates all of this. 18 Claude Code skills. Resume tailoring from your real experience only. Interview prep with insider data from 250 companies. Mock interviews that compound (after 5-6 real interviews, the system knows your weakest question types). Networking sequences. Negotiation.

$49 once. 45-minute setup. Then 20 minutes a day.

Honest caveat: the system is only as good as your experience library. If you skip the 10-minute setup where you load your real career history, every output will be generic. The input is the bottleneck, not the tool.

Full deep dive: news.aakashg.com/p/job-search-os