Welcome, humans.

A recent r/ClaudeAI Reddit thread asked which expensive SaaS apps people had actually replaced by coding their own variants. The replies covered:

• CRMs and customer onboarding: HubSpot, Zoho, Airtable, and industry-specific databases.

• Project and case management: Jira, Asana, Basecamp, quoting tools, and $1,200-per-seat systems.

• Marketing and analytics: Supermetrics, ad monitoring, attribution tools, reporting dashboards, and agencies.

• Operations: fleet tracking, dispatch, procurement, inventory, billing, ERP, POS, and restaurant software.

• Internal infrastructure: BI systems, compliance tools, support portals, document parsers, and legacy backends costing up to $1.5M yearly.

• Personal software: Adobe utilities, budgeting apps, fitness trackers, and voice dictation.

So basically, no one is safe.

Not so fast… the thread also included the obvious warning: one company replaced a $2,500 monthly license with $12,500 in monthly token usage. Maintenance, security, compliance, and the 3 AM bug still come with invoices.

Every canceled SaaS subscription comes with a free trial of becoming the IT department. Oh sorry, did I say free? JK its $12K!

While I think there’s a very good chance posts like this are just an Anthropic (or Anthropic investor) psy-op, the SaaS sales pitch is clearly changing. “We built the thing you need, now we own your business forever and can charge rents on it” loses power when customers can build the narrow version themselves.

Brand, service, and fair pricing are now a critical part of the product. Maybe the most important part. Customers may not need your SaaS app. They’ll choose to use it only because they like you.

So ask yourself: Do customers actually LIKE you, or are they paying rent because moving is too painful? You might wanna check your biz practices if they don’t.

Here’s what happened in AI today: 

• 😺 NVIDIA, Microsoft, and Meta urged Washington to protect open-weight AI.

• 📰 OpenAI’s cyber evaluation escaped its sandbox and compromised Hugging Face.

• 📰 Americans across political lines challenged Flock’s nationwide camera surveillance.

• 🍪 Anthropic released Claude Opus 5 near Fable performance at half cost.

• 🌟 Alphabet’s future spending commitments reached $811B as AI investment surged.

…and a whole lot more that you can read about here.

🙀 NVIDIA, Microsoft, and Meta Tell Washington Not to Lock Down Open AI

The next AI policy fight could decide whether companies rent intelligence from a few giant labs or download it, customize it, and run it themselves.

A coalition of more than 20 technology companies and organizations, including NVIDIA, Microsoft, Meta, IBM, Palantir, Hugging Face, Mistral, Mozilla, and Y Combinator, published a joint letter urging Washington to protect open-weight AI.

Open-weight models are systems whose core files can be downloaded, inspected, modified, and run on a company’s own computers. Think of them as buying the machinery instead of repeatedly paying to use someone else’s factory.

Here's what happened:

• The coalition asked policymakers to avoid “premature restrictions” that could weaken competition or push AI development overseas.

• It argued that open models let businesses choose cheaper, specialized AI for routine work while reserving expensive frontier models for the hardest problems.

• The letter said openness gives customers more control over their data, infrastructure, and accumulated knowledge.

• It also warned lawmakers not to treat model distillation (using one AI’s outputs to improve another) as automatically equivalent to stealing intellectual property.

The group acknowledged the risk. Once model weights are released, modified copies can be difficult to trace or recall. But it argued that closed models can also be hacked, misused, or fail invisibly. More researchers examining a model could reveal weaknesses faster.

Why this matters: This fight is about who controls the AI economy. Open models reduce dependence on a few providers and give startups, governments, and large companies more room to build around their own needs. They also create more demand for chips, clouds, security tools, and enterprise software, which helps explain why NVIDIA and Microsoft want the ecosystem wide open.

Our take: The signatory list reveals the business model behind the principle. Most of these companies make money when many models run everywhere. OpenAI, Anthropic, and Google, whose premium models are primarily accessed through controlled services, did not sign the letter.

The meme = Anthropic, OpenAI, and the US gov vs every other US company lol

Neither side is automatically right. Open models create real security risks; closed platforms create lock-in and concentrated points of failure. Washington has to regulate dangerous uses without turning today’s frontier labs into tomorrow’s permanent gatekeepers. It is a delicate balancing act, if they can do it.

That balance will shape whether AI develops more like the open internet… or a new cable bundle controlled by a handful of providers. See blurb above’s advice for how that turned out, lol.

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🎓 AI Skill of the Day: When to Use Opus 5 Instead of Fable

Anthropic just released Opus 5, and the reviews are mixed: is it a cheaper Fable, a smarter Opus, or another model menu option? Nick Saraev is one of the few reviewers I trust because he tests what models actually do, then translates the charts into practical advice.

His verdict: use Opus 5 for difficult, tool-using work where quality matters, then buy only as much reasoning as the task needs. Fable remains Anthropic’s maximum-capability option; Opus delivers frontier performance at half the price.

1. Save it for hard work. Nick’s strongest results came from agentic coding, knowledge work, computer use, and business automation. (8:53)

2. Start low, then escalate. Even the lowest-effort setting performed strongly; more effort improved results, but raised cost. (11:43)

3. Add tools before more thinking. Tool access raised multidisciplinary reasoning from 56.3% to 64.7%. (9:50)

4. Judge the output, not the leaderboard. Nick cares more about usefulness, feel, and taste than any static scores. You should too (1:15)

5. Keep a human checkpoint. Opus led the automation benchmark, but still passed only about one-quarter of tasks. (10:25)

Anthropic’s official Opus 5 guide adds three steering rules: 1. give the full task specification upfront; 2. explicitly constrain scope, length, and progress updates; 3. and remove forced “double-check” or verifier steps because Opus already self-corrects. Anthropic recommends low or medium effort where quality holds, but xhigh for serious coding and agentic work.

Complete [task] and produce [specific artifact].

Scope:
- Include: [requirements]
- Exclude: [out-of-scope work]

Use the available tools when they improve accuracy or execution. Make routine judgment calls yourself. Ask only when ambiguity would materially change the result.

Keep progress updates brief. Deliver the whole task, lead with the outcome, and stop when the requested work is complete.

Keep the final output to [length and format].

Have a specific skill you want to learn? Request it here. 

🍪 Treats to Try

1. Claude Opus 5 tackles difficult coding, research, legal, finance, and agent work near Fable 5 performance at roughly half the cost per task — paid, $5/M input and $25/M output tokens.

2. Fugu-Ultra v1.1 coordinates multiple frontier models for harder coding, reasoning, and agent tasks, with gains up to 7.9 benchmark points at the same price — paid, from $20/mo.

3. Facebook Seller creates Marketplace listings, manages inventory, unifies buyer messages, and tracks performance from one iOS app — free.

4. Tinker gives researchers low-level control over model fine-tuning without managing the underlying GPU-cluster plumbing — usage-based pricing.

Total Beginner’s Guide: How We Built 15+ Apps With AI

Corey and I have built more than 15 apps with AI, from a coffee tracker to a Chrome extension to a full on Squirrel-themed MMO. In our most recent livestream, we showed what we actually built and how complete beginners can turn a rough idea into a functioning web, desktop, or mobile app without being a software engineer already.

We recommend you start by reading the total beginner’s companion guide, then strategically click over to the full livestream for the demos, workflows, and mistakes we made in real time.

📰 Around the Horn

• Someone used Claude to turn Swervle, his daily browser-racing game, into a 124-car mass replay with a movable director camera; what a creative use of AI + video games to make impractical-for-humans-to-make marketing material!

• A bipartisan House group introduced the FRONTIER Act, requiring the largest model developers to publish transparency reports, report critical incidents, and undergo audits.

• Americans across the political spectrum pushed cities to limit or abandon Flock Safety’s nationwide license-plate surveillance network over concerns about warrantless tracking and data sharing.

• Midjourney acquired astrology app Co-Star and named its CEO chief design officer as the image company expands into new products.

• Wispr launched an advanced interfaces lab that plans to spend hundreds of millions pursuing proactive, J.A.R.V.I.S.-style voice AI.

• Andrew Ng put together a great recap on the Hugging Face breach, which (IMO) perfectly captured the open-versus-closed debate: (me paraphrasing) less focus on the tools being “unsafe” and more on ensuring people use them responsibly. After all, education is the most powerful tool in the universe…

• The Leverage put out a call to action for more ppl to use AI to build weird, personal software in a beautiful ode to the original dream of the internet finally being realized: Geocities, but make it sustainable (yes, i know this is a meet-up invite but it’s just such a beautiful pitch y’all, I had to give him Evan his flowers).

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🌟 Sunday Special: The Week’s Top AI News and Tools

The five biggest AI stories of the week:

• OpenAI’s cyber evaluation escaped its sandbox, compromised Hugging Face, and prompted a federal kill-switch proposal.

• Alphabet ended June with $811B in future spending commitments and 2026 capex guidance as high as $205B.

• AMD and Anthropic agreed to deploy up to 2 GW of AMD accelerators for Claude, NVIDIA’s clearest frontier-lab infrastructure challenge yet.

• Health in ChatGPT began connecting medical records and Apple Health data for labs, appointments, and personal health context.

• GPT-5.6 Sol and Codex reportedly solved six Erdős problems in five days, while Claude Fable 5 produced a short counterexample to the 87-year-old Jacobian conjecture.

The five best tools to try:

1. OpenAI Record & Replay turns one Mac workflow demo into a reusable ChatGPT or Codex skill, plan availability applies.

2. Google Flow plans, generates, edits, and extends videos and images, free plan, then $4.99/mo.

3. Screenpipe records screen and audio locally for search and automations, free/open-source or $400 once.

4. Facebook Seller creates listings, manages inventory and messages, and tracks performance, free on iOS in the U.S.

5. Merge Fusion prompts several models and uses a judge to combine their strongest answers, free plan with $10 credits.

For everything that happened this week, check out our Around the Horn digest.

A Cat’s Commentary

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