POV: You're a Product Manager ☕️

95% of PMs can't really code. Claude and Codex didn't change that. What they changed is that those PMs can now create code instead of writing it. The 5% who were already decent engineers make "just build it" look easy, so everyone else tries to follow and ships output they can't fully reason about. The bottleneck was never typing the code. It was knowing what to build. Making code cheap to produce doesn't help you if you're pointed at the wrong thing. You just get there faster. Roadmaps have the same problem. Now that Claude generates one in seconds, people ask whether dedicated roadmap tools still earn their place. They're asking about the cheap part. A roadmap is a visual summary of decisions you already made, arranged on a timeline. Claude produces a clean one. A roadmap tool produces a clean one. The artifact was never where the value sat. Most of the value sits in the process around it: sync, ownership, accountability, links to work items, statuses, dependencies, the record of what changed and why. That's what you're paying a roadmap tool for, and generating a document doesn't replace it. The rest sits in a layer almost no tool handles. A good roadmap shows why you decided to build something, not just what. What customers say on calls. What keeps coming back in support. What stalls deals in the pipeline. What's already half-built in your dev tracker. This is where the AI conversation lost the plot. The energy goes into token maxxing: faster generation, more output, cheaper tokens, more documents an hour. But a roadmap built faster isn't a better roadmap. Impact maxxing is the thing that matters. Did what you built move the metric you cared about. That comes from the context feeding the decision, not the speed of the artifact. And it forces a question: how do you know you put effort where it actually matters? Right now the answer is a PM squinting at the output and deciding it looks about right. Same trap as the code they can create but can't reason about. So the answer for the 95% isn't more code or more roadmaps. Go back to the signals. The feedback. What customers and support and the pipeline keep telling you. Make sense of it well enough to act on. That's the layer we built at Bagel. We pull scattered signals into structured decision context, so when an AI tool generates the roadmap or the brief or the ticket, it works from what's real instead of what happened to be in the prompt. Speed was never the problem. Producing the wrong thing quickly was. The win is reaching the right thing, the right way, at the right time, faster. @ohadbiron

“we shipped 40% more this quarter” is not the flex you think it is. shipped what. for whom. did it matter. (it did not matter.)

They speak the true true

"Can't we just build an agent to connect all our systems?" Sure. Just connect Slack, Zendesk, Gong, Jira, and the rest. Keep the integrations from breaking when their APIs change. Dedupe the feedback. Cut it by ARR, by ICP, by how often it gets asked. Tune the prompts. Catch the hallucinated themes before they hit the roadmap. Re-eval every time the underlying model updates and quietly drifts. Close the loop back to the field so nobody's input dies in a void. And don't forget to hand it all to Claude so it actually builds the right thing, or stand up an MCP server for that too. Then maintain the whole thing forever, around the roadmap your analytics team already has. Or you can just Bagel it: lnkd.in/deyudixq

Somewhere right now, an engineer is building something nobody asked for. Fast. That's the thing about AI. It'll build anything you point it at. Point it at the wrong thing and you get a beautifully engineered mistake. Bagel figures out what's actually worth building, so the thing that ships is the thing customers were asking for all along. Fewer rebuilds. Calmer retros. An occasional early weekend.

Everyone's saying MCP. Loudly. (Some of them know what it means.) Your AI agents each know exactly one thing: Cursor knows your codebase. Claude Code knows your docs. Glean knows your wiki. Not one of them knows a single customer, which is awkward, because customers are the reason any of it exists. MCP fixes the introductions. We wrote up what changes for product teams once your agents stop guessing, and yes, where Bagel fits: bagel.ai/blog/how-mcp-is-cha…

Yes sir, we can boogie, but we need a certain signal.

We're Here

PRODUCT MANAGERS: YOUR JOB IS TO BUILD GREAT PRODUCTS. 🥯 Meetings ≠ progress. Align without endless check-ins. 🥯 Roadmap > reactive work. Focus on impact. 🥯 Clear insights, not chaos. Spot real patterns. 🥯 Less admin, more decisions. Reclaim your time. Build what matters.

We pitched at The Pitch by @deel. We're going to Paris. Building Bagel AI from the ground up, I've learned that most product & engineering teams are fast. And now even 100X faster. Building the wrong things. The decision layer is broken. Too much noise, too little signal, and by the time anyone agrees on what to build next, the moment's passed. That's the problem we built Bagel to solve. And apparently, the judges agreed. Out of thousands of startups competing globally, Bagel AI won the semi-finals $50,000 prize and is now heading to the Global Finals in Paris, competing for a $1M investment alongside some of the world's best early-stage founders. Excited. Focused. Ready. The French have their baguette. We're bringing them Bagel. See you in Paris. 🇫🇷🥯 @ohadbiron

2 minutes to pitch Bagel AI on Pitch by @Deel. @ohadbiron somehow made product velocity sound exciting in less time than it takes to make coffee. 37:30.

We kept hearing “why can’t we just use Claude for this?” So our Head of Data & AI Neta Barkay dug into the research and our own data and wrote the answer. Spoiler: Claude writes a great summary. Your product team still ships the wrong thing. Read Here: bagel.ai/blog/your-ai-knows-…

We just killed the Copy-Paste PM. Starting today, you can push ideas straight from Bagel into Jira, Linear, Notion, Airtable - you name it. Each idea lands with the number of customers behind it, ARR impact, and a direct link back to the full evidence. One click. Full context.

This whole episode is a masterclass. Geoff's take that "your job is to automate your job" hits different when you realize most product teams are still manually stitching together customer signal from 10 different tools. That Ramp VoC agent demo? That's the future for internal teams with engineering resources to build it from scratch. For everyone else, that's literally what we do at @trybagel We already pull from Gong, Salesforce, Zendesk, Jira, Slack and connect it all to revenue impact automatically. No internal build required. MCP and a lot more shipping soon. The L0-L3 framework applies to product intelligence too. Most teams are at L0: sometimes checking a spreadsheet. We get them to L3: evidence-backed decisions on autopilot.

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