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Designing for agents as the user
On Y Combinator's channel, Supabase's founder said signups are accelerating, and activation and conversion are rising with them. She said that usually doesn't happen when the top of the funnel grows. She credits models that are now good at building, which is bringing in people without engineering backgrounds . Software engineers are still a large share of users, but this "builder" segment is growing fast .
The product followed the users. After seeing that "no one visits dashboards anymore," she sent the team a memo: the product must be code-first, and anything on the dashboard must also work in code as infrastructure-as-code. The team rebuilt its CLI to match . She describes two stages of winning agents. First the model has to pick your product. Then, when an agent gets stuck, you need to keep it from switching away. So the error messages now tell agents how to fix the problem or which docs to read . Much of the engineering effort now goes into testing specific use cases across models, not into building .
Outcome pricing in vertical AI
On a16z's channel, Eve, a legal AI company for plaintiff firms, said it wants to handle a case "from the very first call to getting them a settlement" . One Los Angeles firm reportedly went from about 100 clients per attorney to 500, using Eve for intake, qualification, handoffs, and settlement prep . Eve started with tiered, Starbucks-style pricing. It moved to outcome-based pricing about a year and a half ago, once it learned firms cared most about serving more clients. It expects billable-hour pricing to fade the way seat pricing has, though not to disappear where value only shows up years later . Eve says it hasn't yet run a fully autonomous end-to-end case. Human touches per case are still needed but are "drastically" falling month over month .
PMMs say AI is creating rework
On r/ProductMarketing, a PMM said she spent about 70% of her time this year editing other people's AI-generated content. Some of it was misleading or didn't match the company's messaging . Replies converged on one rule: whoever generates the copy has to check its product claims, or "they've just moved their work into your queue" . Another PMM says AI gets 80–90% of the way and makes people check with them before using AI assets . One commenter said they're measured on how much they use AI and fear being cut if they don't use it enough .
The strongest workflow shared in the thread watches 30+ cybersecurity competitors. When a relevant breach happens, it drafts a point of view for a human to approve, and only then generates posts, emails, and discovery questions. The author calls the results "a pretty good draft" .
Win/loss: price objections often mean the buyer couldn't justify the premium
A B2B SaaS PMM found that CRM notes blamed price for lost deals. Buyer interviews told a different story: buyers understood the product but couldn't explain internally why it was worth more. That came up in only two interviews, so the PMM held off on rewriting the battlecard . Suggestions from commenters:
- Ask recent wins how they got the extra spend approved .
- Test a short internal-approval page in the next few deals, showing the price difference next to the cost or risk the product removes .
- Ask buyers who saw it and whether it changed the decision. A forwarded link or a click doesn't show that the objection moved .
On AI-run A/B testing, one reply had a similar view of division of labor. Use AI to design experiments, check variables, and compute significance, but keep a human in charge of decisions .
Heuristics worth keeping
- Prompting. Aakash Gupta relays Claude Code creator Boris Cherny's three rules: say what you want, set the effort level, and define how the work is verified . Gupta notes that Opus 5.5 Max costs about 4× more per task than medium and scores 58 vs. 48 on the Artificial Analysis index. He often prefers lower effort with a few rounds of iteration .
- Launch discipline (Mark Pincus, via Gupta). Only one new idea per launch, so the launch actually tests it. Users should reach "the good stuff in 3 clicks." Set your click-through bar before you look at results .
- Context. Run the Business frames product intuition as compounded context: PMs have to "out-context" their CxO to hold their own in a debate .
- Retention ethics. On r/startups, a founder said they design retention around addiction-relapse triggers and streaks . Commenters called it "engineering a dependency loop" and pointed out the post gave no churn numbers .
- The company reports its user base expanding beyond software engineers to nontechnical “builders” using AI to make their own tools; signups, activation, and conversion were all rising as models improved at building.
- Its product strategy evolved from dashboard to API to code-first: agents favor CLI and code workflows, so the team reworked its CLI and made dashboard capabilities available in code and infrastructure-as-code. It also plans a Spark tier for prototype workloads, using an architecture compatible with PostgreSQL and SQLite, with a path to scale up.
- For agent-facing UX, error messages should explain how to fix a problem or where to find guidance, with iterative improvements to help agents recover instead of switching tools. The company evaluates multiple models and invests engineering effort in testing specific building use cases.
- To keep pace with fast-changing product needs, the company dogfoods new tools, reserves team capacity to respond to ecosystem partners, and experiments while recognizing that some bets will fail.
- Aakash Gupta relays nine product heuristics attributed to Mark Pincus : start with proven patterns, improve one thing, and bet on one new idea ; use the “95/25” rule as a reminder to trust instinct but test ideas ; classify a change as new if 10/10 existing users would not choose it ; and isolate one new idea per launch so it can be tested . The post’s “magic trick” example says Jobs and Gates both copied from Xerox PARC, but Jobs made it seem as though Gates was copying him .
- For product UX and launches, get users to value in three clicks, not three minutes ; ship less additional novelty early in a platform shift because the platform itself is new ; test top-of-funnel demand before building on an untested notion of viability ; and set the click-through success bar before reviewing results .
- For AI-assisted work, Claude Code creator Boris Cherny’s prompting guidance is to specify the task, effort level, and how to verify the result . Use clear, conversational instructions, define what “done” means, and choose an evaluator suited to the task; the author’s example is a skeptical PM with 5–15 years of experience . The post reports that Opus 5.5 Max costs about 4× as much per task as medium and scores 58 versus 48 on the Artificial Analysis Intelligence Index; the author often prefers lower effort with iterative rounds . It recommends moving from long prompts toward harness thinking, then prompting relatively simply .
- Value proposition and workflow: Eve positions its legal AI as a way to expand revenue by relieving plaintiff firms’ labor bottleneck, rather than simply cutting costs; it aims to handle cases from the first call through settlement while bringing lawyers in when needed. One Los Angeles firm reportedly increased the number of clients managed per attorney from about 100 to 500 using Eve for intake, qualification, handoffs, and settlement preparation.
- Pricing: Eve says it started with seat-based pricing, then moved early toward outcome-based pricing after learning that firms valued serving more clients; it expects seat or billable-hour models to remain useful where outcomes are hard to price, but sees outcome pricing as fitting work with measurable results.
- Differentiation and maturity: Eve argues its value comes from applying case-specific and local legal context—not just raw model intelligence—and says it never trains on firms’ privileged case data. The company says fully autonomous end-to-end cases have not yet been achieved: cases still require some human input, which it says is decreasing month over month.
- The founder’s edtech marketplace had 50+ mentors listed but struggled to generate qualified leads; he concluded it needed more traffic and time, then shifted toward a cash-generating service model. Conversations with 500+ people surfaced demand for internships and placement support, prompting an internship partnership and in-house mentors and recruiters; he reports 6x ROI for the September 2026 program and plans to run 10 cohorts before trying something new.
- A commenter recommends breaking down that 6x ROI before scaling: track cost per enrolled student by cohort and enrollment sources (free calls/webinars versus ads); another warns ad costs may rise as the most likely buyers in a narrow market are reached. Commenters also advise checking results after refunds and ad spend, and defining placement-support terms and duration in writing to reduce expectation-related refund risk.
- Another commenter suggests using service delivery as product discovery: deliver the outcome manually to find customer friction, standardize repeatable workflows, then automate progressively and reinvest the margin in the product.
Hiten Shah argues that anyone can use AI to build useful software , citing a distributor who photographed his stepfather’s paper contact sheet, fed it to ChatGPT, and now has a CRM in Notion .
The episode frames product intuition as context compounded over time: insufficient context is a source of product-team frustration, and PMs need enough context to hold their own in debates with C-suite leaders. It emphasizes building context regularly without getting lost in process mechanics. Related reading highlights deliberately trying to disprove ideas and gathering context before committing as ways to strengthen discovery and avoid building the wrong thing.
After six weeks with a working product, Hiten Shah put it in other people's hands; what the team learned changed the product they launched the following week. He planned to share the early-access process for others to use.
- AI can connect CRM, support, billing, product analytics and marketing data to help PMMs understand ICPs, customer behavior, adoption gaps, competitors and product performance, then support research, positioning and GTM planning.
- One PMM automated competitor monitoring across 30+ cybersecurity companies to draft positioning and sales enablement; breach-triggered workflows generate a POV for human approval before creating GTM assets, while Gartner MQ monitoring helps inform positioning and roadmap influence.
- Keep humans accountable for accuracy and differentiation: one PMM says AI gets 80–90% of the way but needs human polish, while another reports spending about 70% of the year editing AI-generated work that was sometimes misleading or off-message; a commenter warns that leaving claim-checking to the reviewer shifts work into their queue.
Hiten Shah argues that future marketing will favor companies willing to let the market change their product.
A non-tech MBA student said their product internship rejection was attributed to a situational interview answer that lacked context about the company’s business; with 2–3 months before graduation, they also reported few campus PM roles and difficulty finding remote internships.
- Awareness of PM openings varies by company: teams may be told early or get an internal-only application window, while other organizations keep hiring quiet until the public posting; smaller teams may be more likely to know.
- One PM hiring manager’s view is that an APM referral is more likely to ensure a candidate’s CV is considered than to sway a director’s decision; openings may also sometimes be informally earmarked before public posting.
A SaaS team says Zoho Subscriptions is limiting as its billing grows more complex, and is seeking support for trials, plan changes, recurring payments, invoices, cancellations, and self-serve account management without workarounds . One commenter says their team uses Zuora; another suggests Recharge .
For AI-assisted A/B testing, a practical division of labor is to use AI to speed experiment-design drafting, check that variables align with intended outcomes, and measure statistical significance, while keeping a human responsible for decisions . Multi-armed bandits are an established technique, not a new development .
In a B2B SaaS win/loss review, CRM notes pointed to price, while interviews found buyers understood the product but struggled to justify its premium internally; the poster had heard this from only two lost buyers and was cautious about changing the battlecard based on that small sample.
To investigate the gap, commenters suggest asking recent wins what evidence and materials helped them secure approval for extra spend, keeping questions open-ended, and checking what buyers actually saw and what questions or decision effects followed; a forwarded link or click alone does not show whether the price objection moved.
Rather than treating a battlecard revision as permanent, test a short internal-approval page in a few deals, pairing the price difference with the specific cost or risk the product removes, then use buyer and sales feedback to pressure-test the message.
In a discussion about leaving PMM, one commenter says they have moved between PMM and Product Management successfully and recommends staying in the same industry; another sees PMM as a useful pivot into product, though not necessarily a strong long-term career path.
A commenter argues that product-owner roles require domain-specific experience and that generalists are struggling in the current market . In the thread, a laid-off product owner with four years’ experience reported hundreds of applications but few interview calls, alongside rejections and ghosting; a respondent recommended scouterzero and the fintechpm as niche sites with hiring-manager contacts, plus Awiral Bhatnagar’s WhatsApp groups .
- For founder-led outreach, focus on one narrow customer segment and contact companies when a concrete change—such as a new location, job posting, website, or service—gives a relevant reason to write. The suggested cadence is two short follow-ups, about three days and then a week later, before stopping.
- The author corrected the “one hour a week” plan to 10 new companies per week, not per day: roughly 20–25 minutes to identify targets, 25 minutes to write emails, and a few minutes for follow-ups.
- Treat outreach as a learning loop: track human replies and how many become real conversations or calls, rather than relying on opens; the author’s rough diagnostic is that fewer than 1 human reply per 50 well-targeted emails after follow-ups points to targeting or the reason for writing, not wording.
A founder says they apply addiction-recovery insights to business retention, naming triggers, streaks, accountability, and the risk of a first missed day; they call this their best way to reduce churn, but provide no before-and-after figures, a gap a commenter highlights. Another commenter warns that this approach risks engineering a dependency loop, raising an ethical concern for retention design.
A Senior PM built a job-fit screener for job hunters trying to avoid roles that may repeat burnout: users set weighted criteria and dealbreakers, then get a Go/Cautious Go/Skip verdict with explanations, quoted culture or burnout red flags, and interview questions. It also offers company research, resume tailoring based only on the user's real resume, and a job tracker; the resume stays in the browser and is not stored on a server. The builder is seeking 10 testers, each offered 50 free evaluations, with feedback especially requested when the tool gets a verdict wrong.
Baseten released joey-1 (2B), its third open-source decision model, positioning it as the fastest available and among the most intelligent models of its size. It is offered through Model APIs, dedicated deployment, and a fine-tuning cookbook for Training Jobs; Baseten says it targets workloads where time to first token matters, prioritizing speed and cost without sacrificing intelligence.