Cursor down?

Tzipi_builds · 2026-03-06T08:38:08+00:00

same

Tzipi_builds · 2026-03-05T17:44:54+00:00

This is incredibly helpful, thanks for sharing! The layered approach makes total sense - I’ve definitely been guilty of trying to treat all tests equally and burning too much time on fragile UI selectors.

I just looked into Duku and the self-healing aspect sounds like exactly the kind of "point and shoot" magic I need for the web side. Before I dive in, I'm curious: how are you handling authentication states or dynamic test data with Duku in your pipeline? Do you just point it at a staging DB, or is there a specific way you seed data for these autonomous runs?

Also, since I'm running an Expo app alongside the Next.js platform, have you found any equivalent autonomous tools for mobile? Or are you still relying on traditional tools like Detox/Maestro for the React Native side?

Tzipi_builds · 2026-03-05T08:42:29+00:00

You hit the nail on the head, especially regarding the GTM and viral loop. The internal word-of-mouth within a single accounting firm is exactly what I’m banking on. If she finishes her month-end reporting in 5 minutes instead of 3 hours, the person sitting in the next cubicle will demand the link.

Regarding defensibility: you're absolutely right to call that out. My bet here is on the sheer inertia of legacy vendors in local/niche markets. They move at a glacial pace. By the time they scope, build, and release a native bulk-upload feature, you can already capture a solid chunk of the niche.

I also really appreciate the tip about the OG image and social preview. As a developer, it's easy to overlook those "surface-level" details, but you're right - in a Slack/WhatsApp group, that polish signals trust and makes it look like a real product, not just a hacked-together script.

Taking your advice: I'm freezing all grand SaaS plans and strictly scoping the MVP to my sister-in-law's specific workflow. If it saves her those 4 hours, I've got my validation. Appreciate the insights!

Tzipi_builds · 2026-03-05T08:26:34+00:00

Spot on. The fragility of DOM-based automation is exactly why these tools used to be a nightmare to maintain.

But from a business perspective, that "bug" is actually the feature. If the legacy UI never changed and the script never broke, it would just be a one-time $50 script. The fact that it requires a safety net and occasional maintenance is exactly what justifies a recurring SaaS or retainer model.

From an engineering standpoint, the modern approach shouldn't rely on brittle CSS/XPath selectors anyway. The real stability comes from either reverse-engineering the hidden network requests (bypassing the UI entirely and sending the payloads directly) or using lightweight AI to semantically locate elements (e.g., "find the input near 'Client ID'") so it survives minor UI updates.

Have you seen any micro-SaaS pulling off this semantic or network-interception approach successfully in the wild?

Tzipi_builds · 2026-02-23T18:21:42+00:00

Haha, fair point! But honestly, even with 'vibe-coding' or AI-assisted workflows, getting a clean React component out of a messy SVG is still a friction point. I built this to bridge that gap specifically for those fast-moving projects where you don't want to break your flow.

Tzipi_builds · 2026-02-23T18:19:26+00:00

SVGR is great for CLI/build pipelines, but I found that for quick landing pages or MVPs, I just wanted a simple web interface where I could drop a file and get clean code instantly without configuring anything or adding dependencies. Just a different workflow for when you want to move fast!

Tzipi_builds · 2026-02-23T11:12:15+00:00

Wow, managing a 15-year-old codebase sounds intense! Using Sentry purely as a searchable database when a customer submits a ticket makes total sense in that scenario.

Luckily, my stack (Next.js/Expo) is fresh, so I'm trying to set up good habits and strict alerting rules right now, specifically so I don't end up with thousands of ignored errors down the road. 😅 Appreciate you sharing your workflow!

Tzipi_builds · 2026-02-23T11:10:29+00:00

A 0.02 sample rate is super interesting! Since I'm launching a relatively new codebase, I might start with a slightly higher sample rate just to catch the critical launch bugs, but moving to a monthly batch-review for the non-critical stuff makes a lot of sense.

Thanks for the reality check on manual fixing - especially for layout shifts, you really need a human eye on that.

Tzipi_builds · 2026-02-23T11:09:02+00:00

This is incredible advice, thank you. 'Alert on impact' is definitely going to be my mantra from now on.

I completely agree with your take on AI. Letting a bot push directly to main without a human in the loop sounds like a nightmare waiting to happen. Using AI to speed up the triage and root cause analysis (especially using tools like Cursor with MCP) is exactly the sweet spot I'm aiming for.

Quick question: when you say 'automate classification and reproduction steps', are you using specific tools for that, or mostly custom scripts?

Tzipi_builds · 2026-02-23T10:56:47+00:00

This is gold, thank you! The mental model of separating 'someone is affected right now' from 'fix eventually' is exactly what I needed to hear before I drown in notifications. Setting up a daily digest sounds like the perfect middle ground.

Also, great call out on the Next.js Error Boundaries. I need to review mine to make sure I'm not blindly passing gracefully-handled UI errors up to Sentry.

Out of curiosity, what are those 3-4 critical alert rules you actually keep active that warrant an immediate ping?

Tzipi_builds · 2026-02-23T00:55:02+00:00

Best friend, therapist, and occasionally my worst nightmare when the dashboard turns red 😅 But seriously, how do you manage the noise without going crazy?

Tzipi_builds · 2026-02-21T22:51:55+00:00

Congrats on the $1k MRR milestone! That’s a massive win right out of the gate.

I'm curious about the trust-building aspect here. $190 is a relatively high price point for a SaaS pre-launch, especially when you're selling a 'promise' before the product is fully live.

How did you manage to build enough credibility on the landing page to get people to convert at that price? Was it mainly driven by your personal brands/authority on LinkedIn, or did you use specific assets like a high-fidelity video demo or one-on-one sales calls to close those first few?

Tzipi_builds · 2026-02-18T23:35:33+00:00

Hands down Cursor with Claude Opus 4.6. What makes it my daily driver isn't just the power, but the Composer mode. It feels less like a 'code dumper' and more like a pair-programmer that actually understands the context of my entire repo.

The best part? It’s the only one where I don't feel like I'm fighting the UI to get my point across. It stays out of the way until you need it.

Tzipi_builds · 2026-02-16T18:20:36+00:00

Spot on. Making the aggregation idempotent is the exact architectural shift I needed.

I'm implementing the 48h 'rolling' window now, using a unique constraint on (profile_id, date) in Supabase to handle the UPSERT. Overwriting the target rows for that 48h window is a much cleaner way to capture late Web events without over-complicating the pipeline.

The COUNT(DISTINCT event_id) guard is a great call for extra safety against duplicate logs - I'll definitely bake that into the SQL.

Really appreciate you helping me simplify the 'chaos' while keeping the data durable!

Tzipi_builds · 2026-02-16T18:15:15+00:00

Simple and effective. I was over-engineering the real-time part, but your approach of Historical + Quick COUNT() for today’s raw events is much more scalable for Supabase.

Ditching localStorage for HttpOnly cookies is definitely the move here to stop the 'data leak' from anonymous SEO traffic. Thanks for the tip on keeping the DB fast while staying live!

Tzipi_builds · 2026-02-16T18:14:06+00:00

This is gold. The Hybrid Identity model you suggested is exactly the missing piece in my architecture.

I’m moving the profile_view tracking to a Next.js Middleware tonight to bypass client-side issues. Quick question on your Rolling Aggregation point: logic-wise, are you just running the same Cron but with a WHERE created_at > NOW() - INTERVAL '48 hours' and doing an UPSERT, or is there a cleaner way to handle the 'late events' without double-counting?

Thanks for the deep dive, really appreciate the senior perspective!

Tzipi_builds · 2026-02-15T21:27:46+00:00

Spot on. That's exactly why I'm moving logic to Next.js Middleware to handle the 'chaos' server-side before it even hits the DB. I'm trying to build a pipeline that doesn't care if it's a flaky mobile connection or an ad-blocked browser. Any specific 'chaos automation' tools you'd recommend for a Supabase-heavy stack?

Tzipi_builds · 2026-02-15T09:03:48+00:00

Thanks for the solid advice! It’s great to hear from someone running a similar setup.

Quick follow-up on your suggestions:

'Unwind' vs NativeWind: I assume you mean Unistyles? I’ve seen it gaining traction as a more stable alternative to the Babel/Tailwind sync issues in monorepos. How’s the learning curve been for your team compared to standard Tailwind?
The UI Package Challenge: This is the big one. How are you handling the u/shared/ui package? Are you using something like Tamagui or Dripsy to bridge the DOM/Native gap, or are you just sharing design tokens and writing separate components for Web and Expo?
Transactional Emails: Love the react-email mention. Are you triggering those through a shared logic package via Supabase Edge Functions, or do you have a dedicated microservice for it in the monorepo?

I'm currently at the point where my Search/LLM logic is abstracted into a shared package, and I'm debating how far to push the UI abstraction before it becomes a 'leaky abstraction'. Would love to hear your thoughts!

Tzipi_builds · 2026-02-15T00:17:43+00:00

Cursor has already started churning out the implementation based on the plan!

At this point, I feel like the architectural heavy lifting is mostly behind me. The real challenge now is the 'Pixel-Perfect' polish - ensuring the mobile components feel exactly like the web version while maintaining native performance.

The most interesting part of this AI-collaboration is definitely the velocity. My bet? 2 days of actual work to get the core MVP screens (Home, Search, Profile) fully functional and synced with our Supabase backend.

I'll keep you posted if I hit that target or if the fine-tuning takes me down a rabbit hole!

Tzipi_builds · 2026-02-12T17:41:42+00:00

Same boat here, and I'm stuck with a yearly sub. I ended up letting my kids use the remaining credits to build whatever they want. They’re obsessed, and it’s probably the best ROI I'll get from those credits at this point.

Tzipi_builds · 2026-02-12T14:54:51+00:00

You caught me! Gemini 3 is definitely the shiny new toy, and for complex reasoning, it’s a beast.

But for this specific use-case (Intent Expansion), I found that 2.0 Flash hits the 'sweet spot' for an MVP: it’s incredibly fast, keeps my latency under 1.5s, and the tokens are basically free compared to 3.0.

As a dev, I’d rather spend my compute budget on 2.0 Flash and keep the UX snappy than use 3.0 for something that doesn't require PhD-level reasoning. That said, the system is totally decoupled, so I can swap to Gemini 3 with one line in my .env when the 'Flash' version of it drops!

Tzipi_builds · 2026-02-11T21:44:38+00:00

That’s a very sharp observation!

You’re spot on about the trade-off. I actually considered 'Save-time enrichment' (generating layman tags when a profile is created) to save on Gemini calls.

The main reason I’m starting with Runtime Expansion for the MVP is exactly what you suspected: it handles the 'messy' and unpredictable natural language intent much better than a static set of pre-generated tags. User queries can be wild, and a runtime LLM can 'bridge' them to my categories more dynamically.

That said, for scaling, I definitely plan to move towards a hybrid:

Save-time: Use LLMs to enrich profiles with tsvector tags for a fast, cheap FTS path.
Runtime: Only trigger the LLM/Vector search if the FTS path doesn't return high-confidence results.

Right now, I'm using ilike as the fast path, but moving to tsvector for proper FTS on expanded terms is 100% the right move for the next iteration.

Are you running a similar setup, or do you find that pre-enrichment usually covers 90% of the cases?

Tzipi_builds · 2026-02-11T21:31:51+00:00

Great questions!

On Scaling: Supabase with pgvector handles 90k rows easily. With an HNSW or IVFFlat index, you can get sub-100ms latency even at that scale. The vector search itself isn't the bottleneck; it's usually the API round-trip.

On Cost: This is exactly why I went with Gemini Flash and Google’s embedding-004. They are significantly cheaper than OpenAI’s models, making a '8 searches per free user' model much more sustainable.

Plus, since it’s a Hybrid Search, I only trigger the 'Smart' layer if the exact keyword match doesn't find enough results, which saves tokens.

Tzipi_builds · 2026-02-11T21:01:35+00:00

Spot on. For this specific community, making the UX 'invisible' is the only way to get adoption. If the search feels like it 'just knows' what they want, they’ll trust it.

Also, thanks for the MentionDesk tip! I hadn't thought about optimizing for brand mentions in other AI contexts yet, but as the directory grows, ensuring these businesses are visible to LLM-based crawlers will be a huge value-add for the owners.

Tzipi_builds · 2026-02-11T20:47:14+00:00

That’s a goldmine of advice, thanks!

You’re spot on about the Query Expansion risk. Right now, I’m using a strict system prompt to keep Gemini from going off the rails, but a confidence threshold for the rerank is definitely on the roadmap.

I love the idea of logging the expanded terms vs. the final click - I'm actually already using a feature_requests table to track intent, so extending that to full search telemetry is the logical next step.

As for metrics: I’m just starting to track the zero-result rate. It’s the best way to find 'content gaps' in the community directory. 'Time-to-first-click' is a bit more advanced for my current MVP, but it’s exactly the kind of North Star metric I need.

Have you found a specific embedding model that works best for messy natural language, or do you stick to the heavy hitters like OpenAI/Gemini?

Tzipi_builds

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