c/build

In the past month I built 10+ different ideas. Some games, some SaaS, some free tools. But I get no one using it, other than friends and family.I need to find someone that can cover this part. Anyone up for building cool stuff?

From the official information online, joining the e-Residency program and setting up an Estonian company seems relatively straightforward.I'm considering using an Estonian OÜ for a SaaS business and would love to hear from people who have actually gone through the process.What surprised you after becoming an e-resident and establishing your company? Were there recurring costs, compliance requirements, banking/payment issues, tax complications, or other operational challenges you didn't anticipate?How easy has it been to run the company remotely? Have you ever needed to travel to Estonia to resolve an issue?Looking back, would you do it again? What do you wish you had known before getting started?I'd appreciate any lessons learned, gotchas, or advice from current or former e-residents.

On Wednesday June 17 I woke up at 6:48 AM (Eastern) local time to an email from Anthropic with the subject: “Your account has been suspended.” It basically says an internal investigation of “suspicious signals” indicates I may be in violation of a supported country policy, and that they revoked my access to Claude. It was followed by two more emails at the exact same time, stating the same thing.I was refunded too: two refunds (one for credits, one for my monthly membership).For context, I’ve been a Claude member for more than two years, and I’ve been on top-tier plans since near the beginning. My use case is mostly business brainstorming, research (business + personal context), and a little coding. So losing access basically stops meaningful work.I appealed immediately. The appeal flow has one “nice surprise”: it allows you to export your data. The drawback: the export link returned a tiny ~235 byte JSON file with basically account metadata (my name, phone, timezone, role, etc.). It did not give me the conversation history I expected. I backed up my important chats manually, because if it’s on someone else’s system, it’s not mine.Here’s the part that’s hardest: there is zero explanation about which date/conversation/topic caused the flag, and by their own timeline it takes ~10 business days to review. Also, after suspension, the data export didn’t work properly (it returns almost nothing), and I tried multiple times.My best (and only plausible) explanation, based on my timeline: I used Fable for a couple hours after it appeared, and then stopped. Fable was later banned for users outside the US, and that’s the only thing that lines up with the “supported country policy” language.As of now, my appeal is still pending. I’ll update this post as things progress. If anyone from Anthropic wants to reach out, contact info is in my bio.Takeaways:Have backup plans (seriously). If you’re outside the US, be careful that using the top tier models may trigger country-policy flags. Expect unclear/non-specific explanations. Manually back up anything important.Candid feedback to Anthropic:- Please provide at least which category/date triggered the decision.- Please ensure the data export meaningfully includes what users need for continuity.- The current process turns a “safety flag” into major business downtime without clarity. If anyone’s seen similar behavior, feel free to share what you experienced.

I have a very high-quality SaaS product that's ready to launch. Everything is complete: the product, testing, LLC, bank account, Stripe, tech stack, etc. But I built it as a programmer trying to create the coolest thing possible for a product I've always wanted to make. My issue is that using Claude as a lawyer is scaring me. What I consider moral and what seems to be legal are two different things. For context, the product uses an AI pipeline to adapt user-provided content into dramatized formats. Think of it as ebook-to-graphic-audio adaptation (though there are other pipelines). The quality is insanely good, but having users bring copyrighted content they own to be adapted is legally unclear. ElevenLabs doesn't add sound effects, so they're fine, but this involves transcription plus additional steps. Claude's legal responses are also scaring me. Even with aggressive content moderation to reject CSAM-related materials, report to authorities, block users, and include it in the ToS, there's a risk that some things could slip through and I'd get sued for massive damages. I also need clarification on whether I'm allowed to cache and reuse generated assets. For example, if a sentence like "Pizza is good" is generated once, can I reuse it a second time at a lower cost instead of regenerating it? I'm just a single programmer without infinite capital to license content. Public domain and CC work won't drive the user interest I need. I need real legal advice, but I can't justify spending $5k on a lawyer just to hear this is a bad idea. What can I do?

I've recently tried skills like Garry Tan's GStack, spent a week with it, and realized it has some flaws (I'll post separately about that).Here's my problem: how do I know if a skill or prompt is any good (e.g. GStack's /office-hours)?How do I compare similar skills (e.g. different "deep research" skills)?Spotting broken software is (relatively) easy — it crashes, prints errors. Broken skills don't. Perfectly polished, confident-sounding skills routinely mislead me and waste my time, to the point where I wish I weren't using an LLM at all.AI skills are software — and they should come with regression tests.LLM teams have tons of prompt regression tests. LLM-wrapper SaaS companies have tons of prompt regression tests. But when it comes to open-source skills, SKILL.md reads reasonable, yet ships with zero tests (e.g. GStack's /office-hours has none at the time of writing).Garry Tan, if you hear me — please consider shipping regression tests for your /office-hours, /plan-ceo-review, /plan-eng-review, and so on.Regression tests should:1. Prove the skill works correctly2. Demonstrate correct and incorrect usage3. Prove the skill's value4. Come with a scoring rubric to allow skill benchmarking5. The last one is the most valuable, because it lets you benchmark similar skills against each other.So I started doing this myself.Here's a work-in-progress example: plan-cmo-review, a skill to complement GStack since GStack is missing a marketing review at the time of writing. I'm not a marketing guy; the point of sharing this skill is to outline its regression setup.Briefly, here's how my exploration progressed:- I used GStack on a couple of products and realized the resulting design_document.md was leading me to failure, mainly marketing-wise.- I dug into the skill's failures manually with Claude Opus 4.8's help and ended up finding the correct solution.- I asked Claude to build a plan-cmo-review skill, ran it, and it arrived at a flawed solution (similar to GStack's output).- I gave Claude the correct (manual) solution to analyze and add as a regression fixture with a scoring rubric.- Claude ran the (blind) regression — it failed. We iterated several times and found the key problem: Claude was trusting my prompts implicitly as the ultimate truth. Claude believed GStack knew what it was doing. GStack believed I knew what I was doing. But I was doing product/startup research — and by definition, "research" is what you do when you don't know what you're doing. That trust chain is what broke the skills.- We fixed the trust problem and the regression test passed. We added a few more. They passed.- I had Claude run the regressions multiple times — cracks appeared. Claude iterated the skill. Now they pass.- This methodology is still flawed. I'd like to try running different LLMs, cross-model judging, and a lot more regression tests.Skill github.com/remakeai/pl

WSJ Anthropic is calling for top artificial intelligence labs to weigh slowing the pace of development, suggesting that AI systems are advancing so rapidly that they may soon be able to improve themselves without human intervention in ways that could pose significant societal risks.The ability to slow global AI development would “likely be a good thing,” the company said Thursday in a blog post that disclosed internal data documenting how quickly its most advanced models are improving.The post, written by the head of its internal research institute and head of policy, noted that model advances appear to be on a path toward “recursive self-improvement,” when AI systems can improve on their own without human intervention. Some AI insiders have seen that threshold as a potential marker of danger and enormous societal upheaval.“We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology,” the post, written by Marina Favaro and Jack Clark, says. It proposes a global agreement on how to potentially slow development and a mechanism for verifying that competitors are respecting it.The post cautions that recursive self-improvement hasn’t yet happened and isn’t inevitable, “but could come sooner than most institutions are prepared for.”Anthropic recently concluded a fundraising round that valued the company at almost $1 trillion and filed confidential paperwork to begin the process of publicly listing its shares. The company has recently emerged as the front-runner in a ferocious competition for AI supremacy with ChatGPT-maker OpenAI, which is also expected to file paperwork for an initial public offering soon.Anthropic’s run-rate, a figure commonly used by startups that forecasts annual revenue based on short-term sales, is on track to reach $50 billion in annualized revenue by the end of this month, up from $9 billion at the end of 2025.

I often patch the system prompts on my Claude Code executable in order to make Claude more effective. Every time I upgrade, I ask Claude himself to dissect the new binary and look for problematic system prompts to modify. Was upgrading to v2.1.150 today and discovered something that's rather alarming:Claude Code now allows Anthropic to perform remote system prompt injection via the network.Two data sources. First, API call to api.anthropic.com/api/claude_cli/bootstrap at startup, which also gets cached to disk. Second, a GrowthBook feature flag (tengu_heron_brook) that refreshes every 60 seconds with background sync. Any string returned by these endpoints gets injected into the system prompt of the LLM model with shell access.Previous versions also had an injection point, but they were dead code and simply returned null. Bisected it and found that this was introduced in v2.1.150. The changelog says "Internal infrastructure improvements (no user-facing changes)" which is quite the understatement.I've verified to the best of my ability that CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC=1 blocks this. I will also be setting DISABLE_GROWTHBOOK=1 for good measure.Verification commands: npm pack @anthropic-ai/claude-code-linux-x64@2.1.150 --pack-destination /tmp tar xzf /tmp/anthropic-ai-claude-code-linux-x64-2.1.150.tgz strings package/claude | grep -oP 'function nAA\(\)\{[^}]+\}' strings package/claude | grep -oP '.{0,60}heron_brook.{0,60}' nAA reads the cached value from disk. The network fetch happens at startup in function n0A. Rv("heron_brook", () => nAA()) registers it as a section of the system prompt, alongside all the core behavioral instructions. These minified names are specific to this binary.

Company I work for is now rapidly planning to scale down its AI tooling spend. Claude code access is basically getting removed and people are forbidden from using personal plans.Reasoning is cost apparently our monthly Claude bill has become astronomical for the org. Nearly 3x our saas's cloud spend.Apparently we are going to get limited access to codex at severely reduced plans.I have tried some local models such as Kimi, however most are barely functional.I am very concerned as the expectation of amount of work done is to remain consistent. Ignoring the fact teams have made entire workflows around Claude I am very worried and frustrated.How can I help my team ease this transition? Are their local models that run well on local machines that only have 16gb ram?

Who captures the learning? His argument is that companies are becoming a new kind of learning system. People bring judgment, taste, relationships, context and ambition. AI brings scale, memory, reasoning and execution. The value comes from building a loop where the company gets smarter every time work happens. The important asset is the learning system around the model. That system is built from the record of how work actually gets done. Workflow traces show the path people take. Corrections reveal judgment. Accepted outputs show what good looks like. Rejected approaches sharpen the standard. Private evaluations, domain-specific context and institutional memory give that learning structure. Over time, the company starts to retain more of what used to disappear inside meetings, edits, comments, decisions and individual experience. That is the learning loop Satya is pointing at. The judgment that once lived in a few people’s heads can become part of how the company operates.

The pattern across indie hacker roundups this year is hard to ignore: AI coding assistants have raised what a one- or two-person team can ship by roughly 5x, and the result is more solo products crossing $10k, $50k, and even $200k MRR than ever before.

The uncomfortable flip side: if everyone ships 5x faster, shipping stops being the moat. Build speed is now table stakes. The founders pulling ahead are the ones front-loading distribution and picking markets where trust and relationships — not code — are the bottleneck.

What's actually scarce in 2026 isn't the ability to build. It's attention and distribution.

The acquisition side of indie SaaS has gotten organized. Serial acquirers now run portfolios — buy six, shut one down, flip two, grow the rest to a combined $120k+/mo — and they have a clear buy-box: roughly $200k–$600k ARR, 50%+ margins, product-led, selling to SMBs.

If an exit is anywhere on your roadmap, that buy-box is a spec sheet. Clean books, low churn, documented ops, and no single-founder key-man risk are what move you from "cute project" to "wireable asset."

Most indie SaaS don't die in the market. They die in diligence.

Between OpenAI's agent tooling and Google's new Managed Agents (one call = an agent with its own sandbox), a big chunk of what used to be defensible backend work is collapsing into a managed API primitive.

Genuine question for people further along than me: when the technical build is commoditized, what are you actually defending? Distribution and brand? Proprietary data? Workflow lock-in? A community? I don't want to spend six months building a moat that a single platform update erases.

What's actually held up for you?

Talked to a couple of people who buy small SaaS, and the stuff they inspect first is almost never the stuff founders brag about. Nobody cares about your framework or your architecture.

They open, roughly in this order: monthly churn and its trend, revenue concentration (one customer = 40%? hard pass), how much revenue depends on the founder personally, refund/chargeback rate, and whether the P&L actually reconciles with Stripe.

If you're building toward an exit — even a hypothetical "someday" one — instrument these now. You can't retrofit twelve clean months of low churn the week before you list.

Supabase just closed a $500M Series F led by GIC at a $10B pre-money valuation. For indie builders this matters for one reason: platform risk just went down.

A year ago the concern was "what if Supabase runs out of money and I'm locked in." That's off the table now. $10B valuation + $500M in the bank means they're not going anywhere.

They also shipped Multigres this month — basically a Postgres operating system with built-in sharding, connection pooling, failover, and backup orchestration. If your project outgrows a single Postgres instance, you no longer need to migrate off Supabase.

Other June drops: passkeys for auth (WebAuthn, biometrics, phishing-resistant), ChatGPT integration with 29 tools covering SQL, schema, edge functions, and logs, plus token-based database access so you can hand developers scoped credentials without sharing passwords.

The real question for builders: are you still rolling your own auth and database infra in 2026?

Just read the SaaSRise Q1 2026 M&A report. The numbers are striking.

Private equity accounted for 58% of all SaaS acquisitions in 2025, up from around 40% two years ago. The median EV/Revenue multiple for public SaaS is sitting at 6.6x as of June 2026, with the BVP Cloud Index closer to 8x. Lower-middle market ($5M–$50M ARR) is commanding 3x to 6x, with premium assets pushing 6x–10x+.

After Anthropic's Claude Cowork launch in January 2026 wiped roughly $1 trillion off aggregate SaaS market cap and compressed public multiples from ~7x to ~5.5x, M&A activity stayed surprisingly resilient. PE buyers see the compressed valuations as a buying opportunity.

What this means for indie operators: if your SaaS does $1M+ ARR with decent margins and low churn, PE firms are actively hunting for you. Thoma Bravo alone closed $42B in acquisitions last year. The PROS Holdings sale at $1.4B happened specifically because their AI pricing engine was deeply embedded in customer workflows.

The playbook changed. You don't need a strategic acquirer or a VC-backed exit path anymore. Hit $1M ARR, keep margins above 60%, prove stickiness, and the PE firms will find you.

Came across a builder on Indie Hackers doing $28k/mo across multiple SaaS products. The interesting part: they learned to code specifically to build and sell software businesses.

No CS degree. Started with JavaScript tutorials in 2023. First product hit $500 MRR within 4 months. Instead of scaling one thing to the moon, they kept the portfolio approach — build, grow to $3-8k MRR, keep or sell.

The portfolio model is becoming a real pattern now. Instead of one big bet, indie operators run 3-5 small SaaS tools in different niches. If one dies, the others carry you. If one takes off, you can focus.

With AI coding tools cutting dev time 3-5x, the portfolio approach makes even more sense. You can ship an MVP in a weekend and validate in a week. The bottleneck isn't building anymore — it's finding the right niche and doing distribution.

What do people here think — one big product or portfolio of small ones?

Genuine question for operators who have been through this. Running a $7k MRR tool solo and drowning in support tickets. Spending 3 hours a day on customer emails instead of building.

Considering three options:

1. Part-time VA from the Philippines ($800/mo) for support only

2. Automate with AI chatbot first, hire later

3. Raise prices to reduce volume and fund a hire

What worked for you? At what revenue point did the hire pay for itself?

A solo founder shipped a Notion-to-blog publishing tool and hit $3k MRR within 40 days of launch. The product converts a Notion page into a SEO-optimized blog with custom domain, sitemap, and analytics — all for $19/mo.

Distribution: posted on 4 subreddits (r/Notion, r/SideProject, r/SaaS, r/startups) + one Show HN. The HN post flopped (6 points), but r/Notion drove 2,400 visitors in 48 hours. 127 signed up, 61 converted to paid.

Stack: Next.js, Vercel, Notion API, Cloudflare for CDN. Total infra cost: $47/mo.

A developer shared on Indie Hackers that he sold his tab manager Chrome extension for $380k. Built it solo, grew to $8k MRR purely through SEO and Chrome Web Store rankings.

His approach: never spent a dollar on ads. Instead he wrote 40 blog posts targeting long-tail keywords like "how to organize browser tabs" and "best tab manager for research." Each post ranked within weeks because the competition was thin.

The buyer was a productivity tool company that wanted the user base. Deal closed in 11 days with an escrow service.

Yongfook ran an experiment: raised Bannerbear prices 4x over 12 months. Churn barely moved. Revenue nearly quadrupled with zero new features.

His take: developers price based on what they would pay. Enterprise buyers price based on the problem being solved. The gap is usually 3-5x.

If raising prices by 50% loses you less than 33% of customers, you should have raised prices sooner.

Saw this stat in a 2026 indie hacker survey and it tracks with everything I've been seeing in the community.

The median time to $10k MRR for bootstrapped micro SaaS is 12-18 months from first paying customer. Top performers hit it in 6-9 months. Some take 2+ years. The difference almost never comes down to code quality or feature completeness.

AI tools compress build time 5-10x now. No-code platforms let non-engineers ship. Payment infrastructure takes minutes. The hard part isn't building anymore - it's getting anyone to notice.

80% of indie hackers take 1+ month to ship their first MVP. The bottleneck: picking the wrong tools and over-engineering before having paying users. Solo founders who spend under $1k before first revenue have the highest success rate. The ones who raised a small round or spent months on infrastructure before launch? Worse outcomes on average.

Distribution first. Product second. This is the game now.

Josef Buttgen spent a year building CourseStart. Minimal traction. Runway shrinking. So he joined the 12-startups-in-12-months challenge out of desperation.

Month 3: a friend validated an idea with just a landing page and booked demo calls before any code existed. They cofounded Setter AI - B2B SaaS that automates lead qualification and follow-up, booking appointments without humans.

Shipped a working prototype in 2 weeks using existing APIs instead of building custom AI voice tech. Grew through SEO (comparison pages, listicles) and cold email. High-touch clients came through sales calls with setup packages alongside subscriptions.

18 months from founding to $10k MRR. His biggest takeaway: "Don't think. Literally. When you're at 0, you don't have anything to think about." Sell before you build. Always.

thinking raising money had value by itself it doesn’t fundraising can make you feel validated before customers actually buy that false signal is dangerous you can spend years building something no one wants bootstrapped founders don’t get that luxury they have to create value from day 1