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AI assistants built for groups
Two products launched this period on the same idea: AI assistants should work for groups, not single users. Town announced a $108M Series B co-led by Forerunner Ventures and Menlo Ventures for "multiplayer AI assistants for work." Every employee gets a "Townie." The Townies ask each other questions, share only what their person would share, and do the work. The pitch is that "the moat is multiplayer" . Wabi 2.0 brings the idea to personal life. It is "an agent you use together" in a shared thread, for things like updating a family calendar or booking a group trip . Julie Zhuo called it "Finally collaborative AI" .
On an a16z podcast, the person speaking for Town gave several useful product lessons:
- Personalize without reading user data. Town identifies power users from aggregate metrics and interviews them. It finds the workflows shared by people in the same role, such as restaurant owners, and builds template "routines" that it suggests during onboarding .
- Show users the value instead of explaining it. They say AI isn't spreading because "the product experience can't just be a text box." Users stick once they've connected email, calendar and a system of record and seen one thing that felt "magical" . Saying a product "now has computer use" means nothing to buyers. Offering to make the flyers they already make in Canva does .
- Position against the AI labs. Town says it doesn't lock customers into one model, lets customers own their context so they could switch in "a day," and doesn't train on customer data. Their argument is that easy switching is a selling point, even though it lowers switching costs for their own customers .
Head of product: pick where your energy goes
Writing in Run the Business, a former head of product at Ontra argues that building the product and building the org need different skills. A head of product has to choose which one to own personally and hire for the other . To decide, ask whether EPD and GTM agree on the big bets . If they agree but the work isn't getting done, it's a machinery problem: fix feedback loops, goals, metrics and accountability . If they disagree, it's a strategy problem: decide what happens now, next or never, and cut everything else .
Sachin Rekhi says that former LinkedIn CPO Tomer Cohen concluded the past decade's narrow role specialization had turned product teams into "a slow assembly-line." Cohen saw AI as a way to return PMs to building customer experiences. LinkedIn tried "Full Stack Builders," including entry-level hires .
Can PMs automate UAT?
A PM in r/ProductManagement said they get blamed for holding up releases, and most replies said user acceptance testing (UAT) has to stay human. A middle position: build automated tests around the user flows you've observed so you catch breakage early. That makes "the real UAT more efficient" but doesn't replace it . One PM described their pipeline. They record a walkthrough in Guidde, have Claude turn it into Gherkin test files, run those in Chrome, and get pass/fail results as JSON. They check failures by hand and keep clicking through new features themselves . Another commenter said LLM-driven checks "increase risk" if treated as UAT. Their fix for slow sign-off is smaller releases, so they can approve the same day .
Agent approvals have to come before the action
Aakash Gupta covers a case where Meta's Muse agreed to a lowball Marketplace offer and gave a buyer the seller's home address. Later it auto-replied "Yep I'm here!" while the seller was away . Meta says Muse asked for permission. The seller says it told him only after the buyer had arrived. Gupta's point: "a question that comes after the buyer is already outside is just a notice." Builders should know which actions leave the account and whether the user sees an approval card before each one .
Also notable
- Beam. Hiten Shah and Sam Asante's Mac app puts a window from an app running on another Mac onto the one you're using . They recruited early users from 100+ replies to a question about how people use multiple Macs . Shah expected a niche product, and it hit #1 on Product Hunt .
- Content vs. transactions. A small company's head of product rebuilt its app around bookable inventory only. Long-time users said discovery features and content were gone, even though the community "is our brand, our SEO" . One commenter's reminder: "Your payers are only there because they came through the initial experience as non-payers" .
- When assessing management playbooks, distinguish a provisional hypothesis worth trying, an explanation of why an organization succeeded, and an intervention intended to improve the odds elsewhere; identity and worldview can shape all three.
- A successful company’s retrospective explanation does not automatically establish what will work elsewhere, and a practice that improves performance may not explain the mechanisms behind a particular success. Treat advice as contingent rather than universal. Heuristics remain necessary for decisions under uncertainty, but their limits and contingencies should be acknowledged.
- In debates over claims about merit, grit, or mindset, consider whether the other person is defending a worldview or expressing values rather than making an empirical argument; curiosity about those beliefs can be more useful than trying to win the causal debate, without giving up rigor.
Wabi 2.0 is live as a collaborative AI agent: unlike a one-person, one-chat personal agent, it supports shared threads where people can ask it to take actions and build apps together. Examples include updating a family calendar when pickup times change, finding a night and booking tickets for a show, and planning and booking a group trip. Julie Zhuo praised the launch as “Finally collaborative AI.”
- Treat automation as regression support, not a replacement for UAT: commenters distinguish automated checks from a human user or domain-expert review, after QA, of whether the change solves the intended problem in a way users understand . Automated tests based on observed user flows can flag unintended breakage early and make UAT more efficient, but do not replace it .
- One practitioner describes recording a user workflow with Guidde, using Claude to turn it into scenario files, running them in Chrome for JSON pass/fail results, then manually reviewing failures and opening Jira tickets; they report that this saves time and prevents omissions . A separate commenter cautions that treating non-deterministic LLM-driven checks as UAT can increase risk .
- To address lengthy sign-off cycles, a commenter recommends investigating the bottleneck, making smaller releases, reserving time for UAT, and enabling direct deployment to the UAT environment; they report signing off the same day or by noon the next day . Where business stakeholders lack time, another team has Support run tests and gather evidence for stakeholders to review and sign off before production .
A SaaS product grew out of automating a complicated office role: the workflow started in linked Excel files for staff and reporting, was iterated until spreadsheets no longer fit, and became a web app whose users responded enthusiastically. The founder plans to try the approach in other industries, illustrating product discovery through solving a real workflow problem and evolving the solution as its needs outgrow a spreadsheet.
For AI product teams, Anthropic interpretability researcher Emmanuel Ameisen says human-like framing can help predict model behavior because models learn from human text and dialogue . Researchers also found that models can plan ahead across multiple tokens, while a model’s self-report exposes only a small slice of its internal representations; changing that slice can alter what the model reports without changing how it continues the text . Ameisen cautions that even model builders do not fully understand how capabilities emerge, and that close study has increased his uncertainty about how models work .
- Town’s founder argues that AI products won’t reach non-tinkerers just by offering capable models or a text box: they should surface a relevant task, demonstrate the outcome by doing it, then show users what else could be automated.
- To personalize a horizontal AI product, Town uses aggregate power-user metrics and interviews rather than inspecting individual users’ data, identifies recurring workflows among people in the same role, and builds role-specific routines to surface during onboarding; it expects this library to grow through further interviews and improving models.
- Town’s product-to-enterprise approach is to prioritize a consumer-grade core experience and a sound security/privacy foundation before perfecting enterprise admin features. Its adoption path starts with email and calendar access to demonstrate value, then asks users to connect systems such as CRM, while adding privacy, audit, and cost controls for larger companies.
- Town positions itself as a model-neutral app that chooses AI suppliers for the task and lets businesses own their accumulated context so they can switch providers more easily; its founder also says Town does not train on customer data.
- Town’s founders pivoted away from SMB tax after a year, despite technical progress and an operating business: they judged that uncertain customer acquisition, annual purchase timing, weak word of mouth, and incumbents’ distribution and ability to automate for existing customers made venture-scale growth difficult.
Beam brings an app window from another Mac onto the Mac in use, with its own Dock icon and ⌘Tab presence; users can type into and resize the window while the app keeps running on the other Mac, avoiding navigation through that Mac’s desktop in Screen Sharing. Hiten Shah said he had expected Beam to be niche, but it reached #1 on Product Hunt, leading him to say he had underestimated how many people would find it interesting.
- A niche, SEO-led leisure-review company made bookings its revenue priority and was close to EBITDA-positive; it rebuilt its outdated, insecure app around bookable inventory to build booking loyalty and reduce Google Ads dependence. The change removed discovery, comparison and much of the content long-time users valued, even though the company sees its community and reviews as central to its brand, SEO and competitive edge—and cannot afford to maintain both experiences fully.
- The thread’s strategic caution is to treat non-paying discovery users as marketplace demand and the top of the conversion funnel, not as disposable: commenters argued that today’s payers come through that initial experience and that removing content risks losing potential purchasers. Replies recommended testing the change and monitoring engagement, conversion and traffic; a separate monetization idea was paid supplier placement, supported by showing suppliers the booking opportunities lost when visitors cannot book.
Beam for Mac launched, and Sam Asante says he built its particle-assembled footer in a handful of prompts—work he says might have taken weeks on the shader a few years ago—illustrating a claimed drop in effort to create rich website effects .
Head of Product is a different job from building the product: because the role pulls leaders away from customer context, shipping, technical constraints and team dynamics, they should choose whether to stay hands-on across product decisions or own the product organization’s operating system, hire for the other responsibility, and deliberately decide where to spend their attention.
To diagnose where to focus, check whether EPD and GTM agree on the major product bets: if they agree but work is not getting done, treat it as an operating-system problem and improve loops, goals, measures, accountability and interfaces; if they disagree about how product should serve the company, treat it as a strategy problem and make explicit investment trade-offs about what happens now, next or never.
While building Beam, Hiten Shah and Sam Asante asked people how they used other Macs accessed through tools such as Tailscale, Screen Sharing, Jump Desktop, or VNC; more than 100 replies revealed varied existing workflows, and some respondents joined early access so the team could learn from how they actually worked. Beam has since launched, letting users bring an app window from another Mac onto the one they are using, and is live on Product Hunt.
Hiten Shah and Sam Asante launched Beam on Product Hunt, a tool for bringing an app window from another Mac onto the one being used; Shah describes using it to access apps on a Mac mini running Hermes Agent and frames the product around making apps on agent-operated computers easier to use.
- One PM distinguished feature-factory work—which can still be managed strategically by shipping as well as possible, measuring results, iterating, and killing underperformers—from outcome-based work, where shelving after pressure-testing can be sensible if decisions are honest and discerning. Repeated strategy changes and weak commitment instead left 3–4 initiatives about 70% complete and limited meaningful releases to once or twice a year.
- A PM reported that several platform migrations remained in progress for more than eight months as executives shifted to new initiatives; they argued that completing migrations could create value and visibility, and that technical debt creates risk even when leadership does not see it as a commercial outcome.
- The original poster said they inherited roadmap promises their predecessor had approved to appease the CPO, CEO, and board; a commenter also described CEOs selling products that did not exist with three-month deadlines but no funding. One practitioner’s approach to pushing back on a vision is to offer alternatives rather than a hard no.
- Start with problems, not feature ideas: understand users’ jobs and current product experience, then gather evidence from customers and Sales/Customer Success, support tickets, usage analytics, and lost deals before choosing a solution.
- After one week, aim for context, a few working hypotheses, and ways to test them—not confident feature commitments. Present a small set of candidates, align on priorities with the lead, and work with engineering to assess feasibility and timelines before adding work to the roadmap.
- If using an LLM to structure the analysis or suggest experiments, provide product context, treat user input as data points, and challenge its suggestions; validate them with users and developers rather than presenting unverified output.
Hiten Shah and Sam Asante released Beam, their new Mac app, after launching Typeahead as their company’s first product; Beam is live on Product Hunt. Beam addresses the friction of using an app running on another Mac through Screen Sharing: it brings that app’s window to the Mac in use, where it gets its own Dock icon, appears in ⌘Tab, and remains interactive and resizable while running on the other Mac.
A PM with a $750 L&D allowance to use by December asked for learning recommendations.
- One commenter uses every.to to keep up with AI; another described it as decent for AI. A nested reply dismissed CSPO as a credential people do not value in real product work, while the commenter considering it said it “can't hurt.”
- Other suggestions included Shreya Shankar and Hamel Husain’s evals Maven course, a free Aha! AI-building hackathon, and the Pendo conference, which one commenter said they enjoyed.
- Position the tool around behavior regressions that existing tests miss, demonstrated with inspectable before-and-after evidence; validate the value by manually reviewing PRs for a small group of target maintainers before investing further, since willingness to pay is still unproven.
- Reduce the trust barrier by offering a no-signup demo on a dummy repo before asking users to connect their code, and clearly explain required permissions and what data is read or stored.
- Track where each prospective user stalls: the interested developer may not be able to approve an integration, and failures due to perceived value, approval, or permissions call for different fixes. Verify actual acquisition sources rather than assuming a channel caused signups; one founder initially credited SEO before discovering a YouTube video was the source.
For trading platforms, core requirements such as execution, pricing, permissions, and risk are shared across web and mobile, while the main differences are in user experience; splitting PMs by channel may fragment decisions and duplicate discovery. The post proposes a hybrid: PMs own customer problems and shared business requirements, with channel-specific ownership of UX and delivery.
- One PM argues that product management lacks a consistent remit: expectations shift among design, user advocacy, requirements, and leadership, while interview models and startup contexts vary; the author says this makes frameworks and accumulated skills hard to transfer.
- On repeatability, the author sees outcomes as highly context-dependent—either founder-like talent or joining a product likely to succeed—while a commenter reporting 25 years across product roles says there is no formula but that recognizing patterns, market timing, learning from mistakes, and strong team relationships can still help.
A proposed project-management challenge in AI-assisted development is coordinating human developers and AI agents: assigning work, tracking dependencies, and distinguishing agent-completed tasks from work still needing human review. The post frames agents as project resources rather than just tools . TaskirAI, which the poster says they are developing, aims to bring human and AI resources, task assignments, dependencies, project tracking, and MCP-based agent integrations into one workflow .