proud to have won the analytics league grand finals this weekend! 🫡🫡 hard to be beat when data is your love language and you’re using amplitude’s new MCP server

main reason why startups win: shipping velocity and product quality is inversely correlated with time spent on internal stakeholder meetings.

Amplitude has acquired @useinariInari @frankdotlee and @eric_useinari built a product takes messy, unstructured user feedback and turns it into actionable insights. I've been incredibly impressed with their AI chops while focusing religiously on how to make something great for their customers. I always love working with other founders. We share the same vision for how AI will change product development. I'm excited as they'll not only be continuing their mission, but doing it in a much broader way. They'll be taking the lead on our AI Agents team to work to accelerate the future of self-improving products. Welcome to the team!

discord ceo on what he's doing after stepping down. tbh this is my dream as well 😂

obvious call: agent era: deep research for x

🦊@useInari is building a junior AI product manager. Surface customer insights and product opportunities automatically from your feedback, CRM, and backlog so you can prioritize and build products that users love using. ycombinator.com/launches/Mnz…

wanted to demo and get feedback + roasted on an experimental workflow using @useinari that gets from feedback captured → prd and prompt generated → draft ai prototype or github pr created in a few clicks. eric and I were excited by products like @GitHubCopilot workspace and @cognition_labs's devin moving into general release so we were intrigued by the idea of using inari to aggregate customer and product context then generating instructions for ai coding assistants to create 1st drafts of work for you. so we’ve added the ability to create a new insight or issue in one-click from any individual customer quote. inari will search across all of your connected feedback, look for other quotes that are similar, then generate a 1st draft prd or deep dive. the prd and deep dive is grounded in the customer quotes that inari extracted from your feedback and also comes with sentence-level citations so you can inspect it, verify each source, then make edits yourself. we then added the ability to take that prd and generate an ai coding prompt from the prd and customer context. if you’re a power user of @cursor_ai, @windsurf_ai, @stackblitz's bolt , @Replit agent, or other ai coding tools, you’ll know that the tools do 10x better when provided full context on the feature, acceptance criteria, and any customer edge cases along the way. so this workflow uses the feedback from inari to aggregate all of this context for you into a single prompt. you can dump that prompt into any ai coding tool to better steer the models in building features and solving bugs faster and more reliably. major caveat (!) - we haven’t successfully gotten any of the autonomous ai coding tools to build a feature fully on its own since they’re still incredibly unreliable. but we’re betting that the models and orchestration systems will improve so we wanted to make it easy for product and engineering teams to make use their feedback for steering ai for building better products faster! if you’re a pm prototyping with ai or an engineer relying heavily on cursor and similar tools, we’d love to let you try this out or get feedback on how we can perfect this flow for you!

it's technically only "AI agents" if it's from hayes valley or soma. otherwise it's just sparkling for loops.

props to poodleface on their teddit.netment: teddit.net/r/UXResearch/comm… if your team needs to convert customer feedback and research into actionable insights and issues: @useinari

probably the best reddit comment i’ve ever seen from a ux researcher sharing ways that product teams fail when conducting research: 1. ask about their workflows “When customers have an ecosystem of tools, I ask about the tools they use before going to specifics about a particular one. Sometimes other tools drive expectations. If 7/8 tools present information in one way, then your tool probably needs to mirror that (and certainly not contradict it).” 2. best source of insights: workarounds “My most powerful source of latent opportunities is when workarounds organically emerge from different people that overlap in function. An Excel spreadsheet that every company seems to have.“ 3. stop asking leading questions “Asking leading questions is the most common unforced error I see with PMs. Even when you know better, the investment in a particular outcome leads people to signal the answers they are hoping for in subtle ways: tone of voice, body language. It is not hard to put your thumb on the scale without realizing it.” 4. never ask users if your idea solves their problem “Never ask if your idea solves their problem. Ask them to react to it. Neutral language invites the answer “well, that’s cool and all, but…” A lack of specificity in praise is a consistent tell they your idea isn’t resonating. Be willing to probe for bad news, not just good news. It’s not that people lie: they are just being polite.“ 5. allow for moments of silence “Another subconscious error I see is not allowing for moments of silence. When you are asking someone to reflect on their practice, that’s not something most people do every day. It’s important to modulate the pace of a conversation so it doesn’t feel like an interrogation. Short, clipped answers that lack specificity are a giveaway.” 6. consolidate insights. identify inconsistencies. share learnings. “The more subtle problem is information silos. It’s not exclusive to Product orgs. Sales, Marketing, Support, and Success all have their models of what they think the customer is. When an org collectively can’t agree on what the problems are, then that’s when a UX starts to go off the rails… A lot of my job is pulling in all of these models and finding where there are contradictions. And there always are, because having incentives impacts your perception.” 7. don’t “overfish” your users “I do have a problem with when the customer base is not big enough and it gets “overfished”… Customers get tired of constantly being asked for feedback when it is clear that those asking are not talking to each other. From a customer perspective, they don’t see silos, they just see four research requests from one company and exclaim “enough is enough”…”

always love reading everyone's annual tech predictions so here's mine so you can roast me on how wrong (or right!) i am by EoY 2025: 1. we’ll see the first surge of startups becoming “one-billion dollar, two-pizza teams” small ai-native teams figure out new ways to discover and build products, create distribution, and automate internal ops with ai tools. no startups actually reach $1b valuation with only 1 person, but we’ll see >10 $100M valuation businesses operating with <5-10 people. 2. companies like klarna discover that attempting to rip out legacy tools like salesforce for internally-managed ai tools ends up costing more than their original SaaS contracts due to staffing required to maintain brittle ai-code. 3. openai achieves it’s internal benchmark for agi but won't announce publicly in 2025 since it needs to spend the year prepping for its legal battle with microsoft. google ends up releasing the most “capable” model instead while anthropic slips to second while struggling to gain "normie" consumer mind-share. 4. openai’s upcoming agent product in h1 2025 will end up being a nothingburger throughout 2025 just like GPTs have been. openai acquires zapier or make by EoY and the 2026 next-gen agent will eventually be the consumer “iphone” moment for agents. 7. coding agents that work on tasks autonomously (think cursor and devin) “break out” even further as ai quality and reliability improves. bottlenecks on shipping moves away from time spent coding to time spent clearly defining tasks (maybe in @useinari 😉) and polishing user experience. 8. the narrative takes off that figma faces an existential threat from chatgpt, v0, bolt, cursor, and other coding tools. why waste time mocking anything in figma when it’s faster, easier, and higher-quality to just prototype with code? 9. anti-AI sentiment peaks for non tech-native folks in 2025: - consumers balk at perceived negative climate impact of energy consumption for inference/training (similar to bitcoin mining). - hiring remains stagnant, if not declines, due to reduced headcount needs due to better model quality, latency, and reduced cost being applied to o1/o3-tier models - consumers begin recognizing tell-tale signs throughout social media on how much AI-generated slop has permeated their feeds. 10. with prop 36 passing and recent changes in local politics, sf “comes back” to early 2010’s feeling - “safe-ish”, remains default place to build startups, but not yet reaching 2016-2019 levels of fomo and costs. 11. run-rate for rides taken through waymo beats out uber by EoY in sf. waymo reaches price-parity with uber after transitioning from jaguars to lower-cost vehicles in 2025. 12. crypto stablecoins have their “polymarket” moment in 2025 due to pro-crypto regulatory frameworks finally passing in the US. hot take is startups don’t end up being the ones to capitalize, but block/square does due to pre-existing distribution with smbs. circle goes public in 2025. 13. and just for fun: bitcoin reaches 150K, ethereum stays <5k since solana reaches $400 due to better consumer costs, latency, and experience (this is obviously not financial advice 😂).

b2b product teams constantly say that the key metric when prioritizing backlogs is the potential value of the feature based on accounts requesting it. so we made prioritizing by revenue impact simple and automated by integrating with @salesforce and @hubspot! @useinari now connects to either crm, pulls in account and contact fields, enriches every customer and company that submits feedback into inari with gtm data, then attaches a revenue impact to each insight and issue surfaced. for example, we’ve had 100’s of customers say that a blocker to adopting inari was a missing integration for an app they use to collect customer feedback. after connecting salesforce: i can see 40 distinct companies flagged that problem to us and are worth ~221K revenue impact if we closed them all. i can segment this based on my crm’s deal type to see that ~103K of revenue impact comes from prospects while ~137K comes from active customers. i can export the list of companies requesting the feature, see their direct feedback, then follow-up to close the loop. all of the feedback analysis, issue discovery, and linking requests back to companies in your crm are done automatically by inari so you don’t have to manually manage this process in a spreadsheet. if you're having trouble tying your insights and backlog issues to your crm or want to better prioritize your roadmap based on revenue, we'd love to onboard you!

we’ve been getting constant requests at @useinari for being able to connect feedback analyzed, insights generated, and backlog issues in inari with gtm data captured in crms. so today we’re shipping a new direct integration between @useinari and @HubSpot! doing so lets you enrich all the customers and companies sharing feedback analyzed in inari with your crm fields like pipeline stage, whether a company is new or existing, and even revenue values like arr or deal size. use case 1️⃣: prioritizing issues by revenue impact now every backlog issue, surfaced based on your customer feedback or sales calls, comes with a revenue impact value. we generate revenue impact by taking the list of unique companies requesting that feature based on their linked feedback then adding up the deal amounts captured in hubspot. use case 2️⃣: segmenting metrics by deal stage / type if you inspect any backlog issue, you now get a deduped list of companies and deal segments to better inform prioritization decisions. so i can quickly see total companies requesting a feature, deal value attached to those companies, all segmented by whether they’re prospective, existing, or churned. we talked to a ton of teams and learned that basically every team has their configurations in their crms for which fields are actually used. so we built this integration so teams can customize which specific fields in hubspot to map with customer and company fields in inari. so if I want to use a custom revenue, pipeline stage, or persona field, I can update the mapping on my own then resync. this is just v1 of the integration so we’d love to test this out with more teams + hear ideas on how to make this more useful for product + cx workflows! otherwise keep an eye out for salesforce soon 👀

you can just do stuff and people will find ya

i’ve worked in product + startups for almost a decade and have always lowkey wondered why sales teams get CRMs but product and engineering teams don’t get a simple way to track and follow up with customers 🤔 @useinari already automatically analyzes all of your customer feedback and conversations from @Gong_io, @intercom, and other places. now we’ve introduced structured tracking for customers, companies, and personas! we aggregate all the top quotes, feedback, and linked insights and feature requests for each individual customer and company. this makes it easy to browse feedback history, sort on sentiment and interaction dates, then close the loop on specific feature requests for each customer and company. we’ve also updated our backlog so that every issue tracks ARR or deal size. i can sort issues based on revenue impact, see which individual customer or company segments are driving the value, then make prioritization more grounded in revenue without the hassle of manually linking issues to feedback and CRMs. we'd love testers for making this better - hit me up if you’d like to try this out or have ideas on how to improve!

chatgpt voice made is such a delightful product - being able to brainstorm, interrupt the model, and get high-quality responses back fast is soooo nice. openai has a ton of drama right now but they def deserve their flowers for this launch. kudos! putting my dream running/workout workflow out here: - tell chatgpt to search a specific youtube video, book, or piece of static content - convert it into an actionable podcast for the main takeaways similar to google's notebookLM - listen to it on my run - commit the best takeaways to memory

the easiest way to humble yourself as a product builder is sitting in a room, watching all of your app’s session replays end-to-end, then realizing how confused customers are navigating what you built. feels incredibly cringe while sifting through all the confusion but there’s no better place for figuring out how to get better everyday ❤️‍🩹