Launching today
Atoms
Turn your ideas into products that sell
1.1K followers
Turn your ideas into products that sell
1.1K followers
Atoms is a vibe business team that turns your ideas into business. It researches your market, designs the product, builds frontend and backend, connects auth and payments, and ships a live app you can charge for, not just a prototype
Atoms
PicWish
@zongze_x Congrats! 🚀 looks like a serious step up from just coding. how does the research agent handle niche b2b markets compared to generic consumer data?
Atoms
@mohsinproduct
Great question. For niche B2B, we don’t treat it like “generic consumer trend” research. The research agent starts by forcing clarity on the ICP and buying context, then it looks for B2B-specific evidence and constraints, for example:
ICP and workflow: job-to-be-done, current tools, switching costs, integrations, compliance needs
Buyer vs user: who signs, who pays, procurement or security reviews, budget ranges, sales cycle assumptions
Competitive reality: what incumbents already cover, where the wedge is, and what you can win on as a small product
Distribution plan: where demand actually lives for that niche (communities, partner channels, outbound lists, “pain-signal” keywords)
Validation-first MVP: proposes the smallest shippable test that can get a signal from real teams, not vanity traffic
@zongze_x @mohsinproduct
Thank you! For niche B2B we start with a very specific ICP and buying context, then research around workflows, existing tools, switching costs, and where distribution actually happens for that niche. It’s less “broad consumer trends” and more “who buys, why now, and how you’d reach them,” plus a tight validation plan before building too much.
@zongze_x @mohsinproduct
Thanks so much. For niche B2B we try to start from your specific ICP and constraints first, then layer in domain sources and signals, instead of relying on broad consumer patterns. In practice that means we ask for things like target titles, existing competitors, sales motion, price band, and any “must be true” assumptions, then validate those with focused research loops. If you tell me the niche you have in mind, I can share what inputs get you the most reliable output.
Agnes AI
Race Mode sounds wild. Several AI teams trying the same request and then you pick the winner. That is basically how I wish human teams worked too.
Atoms
@cruise_chen
Love that comparison. Race Mode is our way to make trade-offs explicit instead of locking you into one path. You get multiple approaches in parallel, then you pick based on criteria like speed to ship, complexity, and growth potential. Would love feedback on how we should present the comparison to make the choice even easier.
@cruise_chen @zongze_x That makes a lot of sense. One thing that could make it even clearer is surfacing the trade-offs at a glance : a simple comparison table or radar chart showing speed to revenue, technical risk, long-term moat, and operational complexity for each “team.” Bonus if each option also states its key assumption so users know what bet they’re actually making.
@cruise_chen @zongze_x @malekmoumtaz This is such a great suggestion. A quick “at a glance” trade-off view plus the key assumption per option would make decisions much easier. I’ve shared this with the team, it’s exactly the kind of UX that would make Race Mode more usable day to day.
@cruise_chen @zongze_x @malekmoumtaz This is a fantastic suggestion. We agree that “at a glance” trade offs and the underlying assumption are what make choices legible. A comparison view like a table or radar, plus a clear “key assumption” line per option, would make it much easier to understand what bet you are making. If you have a preferred set of metrics, speed to revenue, technical risk, moat, ops complexity are solid, we would love to align on that.
@cruise_chen Haha, exactly 😄 Race Mode is basically controlled competition without the politics. What surprised us most is where the teams diverge, not just in execution speed, but in assumptions about users, pricing, and distribution. Curious: if you could apply “Race Mode” to a real human team, would you optimize for speed, quality, or contrarian ideas
Atoms
@cruise_chen @malekmoumtaz
Love this. And you’re spot on about where teams diverge, it’s often the assumptions, not the code.
If I had to pick for a real human team, I’d optimize for fastest path to a reliable signal, not raw speed. That usually means a tight MVP, clear success metrics, and a distribution test that can tell you quickly if the bet is real. After that, I’d switch the optimization to quality and defensibility once the signal exists.
@cruise_chen @malekmoumtaz Haha yes. If I could apply it to a real human team, I’d optimize for the fastest path to a reliable signal first, then switch to quality once you know what’s working. I also like keeping one lane for contrarian bets, as long as the assumptions are clearly stated.
@cruise_chen @malekmoumtaz Love this question, thank you. Personally I would optimize for contrarian ideas early, then use speed to test them fast, and only optimize for quality once the distribution and pricing assumptions look real.
@cruise_chen Thank you, I love that comparison. That’s exactly the spirit: parallel options, less politics, and clearer choices.
@cruise_chen Thank you. That is exactly the vibe we want
MGX (Now Atoms)
@cruise_chen Appreciate it. That is exactly the intent: parallel approaches, then a clear winner based on goals and constraints, without meetings. If you try it, I would love to hear what evaluation criteria you wish were surfaced more clearly.
remio - Your Personal ChatGPT
Really like the idea of an AI business team instead of just AI coding. Curious what you think is the sweet spot use case right now, indie SaaS, small tools, or DTC style projects.
Atoms
@lvyanghuang
Great question. Today the sweet spot is builders who want something shippable quickly, especially:
micro SaaS and paid utilities, niche tools, AI wrappers with real distribution plans, and long tail products where research and SEO matter.
DTC can work too, but we’re strongest when the core “product” is software and the loop is research → build → launch → iterate.
@lvyanghuang Thank you! Right now the sweet spot is indie SaaS and small paid tools, especially niche workflows where research and distribution matter and you want to get to something shippable with auth and payments. DTC can be a fit too, but we’re strongest when the core product is software.
@lvyanghuang Thanks. Right now the sweet spot is ideas where research, scoping, build, and go to market need to stay tightly connected. Think indie SaaS and small tools with a clear niche and a reachable distribution channel. DTC can work too, but it is usually more asset and brand heavy, so we see better early wins in focused B2B or prosumer workflows.
MGX (Now Atoms)
@lvyanghuang Right now the sweet spot is indie SaaS and focused B2B tools where the ICP is clear and you can iterate quickly. DTC can work too, but it depends a lot on distribution and creative, so we usually recommend starting with a tight niche and one channel first.
Congrats! Overall this feels like a bold attempt to compress an entire product team into an AI native workflow. Excited to see real case studies and to try it on a couple of risky ideas.
Atoms
@libin_yao
Thank you. That’s exactly the bet: not just “AI helps you code”, but an AI team that can run the full loop from idea to something you can actually ship and monetize.
We’re actively compiling end to end case studies now and we’ll share them publicly soon, including what worked and what broke. If you have a risky idea, drop a one liner here and we’ll suggest a fast MVP scope to validate it.
@libin_yao Thanks so much. We’re working on real end to end case studies now and we’ll share more soon. And risky ideas are honestly a great fit, because the goal is to reduce the cost of testing and learning quickly.
@libin_yao Thank you, really appreciate it. We are aligned on case studies being the proof, and we are working on sharing more real examples. If you try it on a risky idea, I would love to hear what felt most uncertain and whether the agents made those assumptions explicit.
MGX (Now Atoms)
@libin_yao Thanks. Case studies are coming, and I would love to hear what makes an idea “risky” for you so we can help you de risk it with clear assumptions and milestones.
Hedy AI
This reminded me of how scattered early stage work usually is. My workflow improved when research and execution stay connected.
Atoms
@kate_sleeman
Exactly. Early-stage building is usually fragmented across docs, chats, repos, and tools, and the original insights get lost. We’re trying to keep research, decisions, and execution in one continuous thread so the build stays aligned and iteration gets faster. Thanks for calling that out.
@kate_sleeman Thank you, this is exactly the problem we’re trying to solve. Keeping research, decisions, and execution connected is where the workflow really starts to feel “compound.”
@kate_sleeman Totally agree, and thank you for sharing that. Keeping research and execution connected is one of the main reasons we built Atoms this way.
MGX (Now Atoms)
@kate_sleeman Thank you. That is a big part of what we are trying to solve, keeping the reasoning and the build decisions connected so you do not lose context between steps.
minimalist phone: creating folders
In what is this different from V0 and similar?
Atoms
@busmark_w_nika
Great question. Tools like v0 are amazing for fast UI and prototyping.
Atoms is built for the “idea to business” loop: it starts with research and product scoping, then carries that context through build, backend essentials like auth and payments, and finally launch and distribution (SEO/growth). So the goal isn’t just a nice UI, but something closer to shippable and monetizable.
Got burned by spec drift once. If Atoms keeps research and scoping tied to build, auth/payments, and distribution, that's the part that earns trust. Do you show a per-step change log for Race Mode winners? That audit trail makes it usable.
Atoms
@piroune_balachandran
This is such a good call, and I’m with you on the trust angle. Spec drift is brutal.
Today we keep the research, scope, and build plan linked, and we preserve the outputs from each team so you can see why a “winner” was chosen. A true per step diff style change log and audit trail is something we’re actively working toward, because it’s exactly what makes multi agent work usable in real projects.
If you have a preferred format, for example Git style diffs, timeline events, or decision checkpoints, I’d love to hear it.
@piroune_balachandran Totally feel you, spec drift is painful. We keep the research and scope tied to what gets built, and we preserve each team’s outputs, but a true per-step audit trail and change log is something we’re actively pushing toward. I agree that kind of traceability is what turns this from “cool” into “trustworthy.”
@busmark_w_nika Great question. v0-style tools are fantastic for fast UI and prototyping. Atoms is built for the full idea to business loop: research and scoping first, then build with the “messy middle” like auth, payments, deployment, and a distribution plan so it’s closer to something you can ship and charge for.
@busmark_w_nika Great question. Tools like V0 are awesome for generating UI and code quickly. Atoms is aiming to be a full AI business team workflow, research, positioning, product spec, build, then go to market outputs like landing copy and distribution plans, with the goal of turning an idea into something you can actually ship and charge for. If you tell me what you use V0 for today, I can map the overlap and the differences more concretely.
MGX (Now Atoms)
@busmark_w_nika Great question. Tools like V0 are excellent at generating UI and front end artifacts. Atoms is focused on the end to end workflow: research, product decisions, build plan, and then shipping toward something you can actually launch and monetize, with the assumptions made explicit.
Congrats! This is a very ambitious scope. Respect for trying to connect research, build, and go to market.
Atoms
@yehan_xiao
Thank you. That connection is exactly the bet. We kept seeing teams generate prototypes quickly, then get stuck on the unglamorous parts: decisions, integration, launch readiness, and distribution. Atoms is our attempt to make that end to end loop more repeatable.
@yehan_xiao Thank you, I really appreciate that. Connecting those pieces is the hard part, but it’s also the part that matters most for solo builders.
@yehan_xiao Thank you. We know it is ambitious, and we appreciate the respect. Our goal is exactly what you said, connect research, build, and go to market into one coherent loop.
MGX (Now Atoms)
@yehan_xiao Thank you, I appreciate that. If you end up trying it, please tell us what felt most valuable and what felt unclear so we can tighten the experience.