One recurring issue we ve been seeing with Supabase setups is not the database itself, but how credentials are managed across environments. The common pattern looks something like:

• credentials stored in .env files or secrets managers

• multiple environments (dev, staging, prod)

• manual propagation or duplication across those environments

It works, but over time it seems easy for things to drift:

• a key gets rotated in one environment but not others

• a redeploy misses an env var

• credentials get misconfigured during setup or migration

A huge thanks to everyone who tried Pinnacle, commented, challenged us, and shared thoughtful feedback last week
We were excited to finish as the #4 Product of the Day. More importantly, the launch gave us a clear signal about what resonated most.
For anyone new here: Pinnacle turns your iPhone into an AI performance coach. It uses built-in phone sensors, conversation, and wearable data via Apple Health to help you improve focus, resilience, energy, and performance with science-backed tools.
You can download the app here:
https://apps.apple.com/us/app/pi...
A few things that stood out:
1. People want insight, not just more data
One of the strongest themes in the comments was that raw metrics alone are not enough. People don t want another dashboard full of numbers with no context.
They want to understand:
How am I doing right now?
What is driving it?
What should I do next?
That is a big part of how we think about Pinnacle. The goal is not to flood you with biometrics. The goal is to turn signals from your body and behavior into something actionable in the moment.
2. Low-friction measurement really matters
A lot of people responded to the fact that Pinnacle works from the iPhone you already have, without requiring extra hardware to get started. That convenience matters more than we expected.
Several comments also pushed on an important challenge: accuracy and calibration. They were right to do so.
If you are using phone-based measurement, the system has to adapt to the individual, establish a baseline, and avoid overreacting to noisy signals. That reinforced one of our core beliefs: relative progress from your own baseline is more useful than generic scores.
3. Personalisation has to reflect real life
One of the most useful questions we got was whether brain-performance baselines should account for hormonal cycles. The answer is yes.
If you want to understand focus, energy, and resilience properly, context matters.
Personalisation cannot stop at a single static baseline. It needs to reflect the realities of each user s life, including sleep disruption, parenting, pregnancy, postpartum recovery, and cycle-related changes over time.
This is an area we want to keep improving.
4. Coaching works best when it is personalised to state, not just prompts
Another theme was the difference between Pinnacle and a standard LLM.
People have already tried using general AI as a coach, and the common experience is that it still puts too much burden on the user to know what to ask.
What we are building is different: Pinnacle combines conversation with biometric and behavioral context, then guides the right intervention at the right moment.
That might mean breathwork, reflection, a micro-break, or a focused coaching prompt depending on what the system sees.
5. Trust, privacy, and product feel are part of the product
Some of the best questions were about privacy, camera use, and whether the experience feels helpful rather than intrusive.
Those are not side questions. They are central.
We want Pinnacle to feel like a calm, intelligent coach, not another noisy app demanding attention. That means being thoughtful about privacy, clear about how sensing works, and careful about when the product should guide versus when it should get out of the way.
What we re doubling down on now

• Better baseline calibration and more personalised scoring

• Stronger coaching flows based on both conversation and biometric context

• Continued work on attention measurement and training

• Better support for more diverse stress and recovery patterns across users

• Making the product feel lighter, clearer, and more useful from day one

Launch day was great, but the real value has been seeing which parts of the vision people immediately understood, and which parts we need to explain better.
If you have tried it already, I d love to hear: what clicked for you, and what still feels unclear?

Been noticing how quickly vibe coding has become a real workflow lately.

A few months ago most of us were still writing everything manually or just using AI for small snippets. Now it feels like the process has shifted to describing what you want and iterating with the AI until the product behaves the way you imagine.

This week at Jots
Hi community!

Firstly, we want to wish you a Happy New Year!

Guys,
I'm looking for some knowledge workers to get involved in helping to shape Clarity!
If anyone is interested, let me know in the comments!

Most code doesn t last.
But the reasoning behind it should.

Good code review captures intent,
why something exists,
what problem it was meant to solve,
and what tradeoffs were accepted at the time.

I wrote a long essay following a talk I gave at AI DevCon in Brooklyn last month.

It starts out with an anecdote about hunting ChatGPT in December 2022 and goes on to explore what I think will be necessary to thrive as code becomes a commodity:

In December 2022, I hunted ChatGPT on Product Hunt.

It ranked #1 product of the day, then the week, and went on to be named Product of the Year.

Having co-founded a YC-backed conversational AI startup in 2018 (long before LLMs) I recognized in ChatGPT the missing ingredient that would have made that venture viable.

The future we d anticipated had arrived. I could revisit my old problem, or I could expand my area of potency by raising and deploying my own venture capital fund.

I chose the latter.

Three years later, on December 9th, I watched a 24-hour window on Product Hunt cross 500 launches roughly double what I observed throughout the preceding 825 days. Only 13 were featured; most were unremarkable.

The LLM has fundamentally shifted the economics of software development.

As someone with a dual vantage point being the #1 Product Hunter while investing in AI startups I watch the floodwaters rise in real-time.

What s become clear: SaaS is dying; VC is withering . Building software is not uniquely compelling. Code has become a commodity.

What most people miss about commoditization is that when a product or resource becomes abundant, it doesn t just get cheaper. It unlocks new and previously uneconomic uses.

I'm 9 months in, broke, and zero paying users. I built Linko to fix the "conversion deficit" in Linktree, but I'm questioning everything.

Hey everyone,

I m reaching out here because I m hitting the wall and need some brutally honest feedback from people who have been through the same quiet phase.

I m a software developer who spent 9 months and too much of my own savings building a professional link-in-bio tool I call Linko. I honestly believe it solves a critical, expensive problem for agencies and serious marketers, but here I am months after the basic version was ready, with zero paying customers and almost no subscriptions. I feel completely discouraged because I ve invested so much time and money, and I haven t made a dime on it.

We'll discuss building no-code Nano Banana agents that can actually generate on-brand designs for your company.

YouTube thumbnails. Ad creatives. Social media graphics. Blog post covers. You name it!

I m building a platform called AnalyzeWare. It automates a bunch of strategy analysis frameworks like SWOT, PEST, competitor analysis, financial ratios, TRL, ESG and more.

The idea is simple: turn hours of report work into a few minutes.

Consultants can white-label the reports and manage clients inside the platform.

Thanks to the Custom Session Context feature we released last week...

ICYMI, it's a way for your website to "brief" your AI before it talks to your users - more here: https://youtu.be/kcxQc4xYYYM

Founders kept telling me the same thing:

I send demo videos but I have no idea what happens after.

So we fixed it.

There s still a lot of attention on flashy categories: AI agents, creator tools, social apps. At the same time, you keep hearing quiet stories about people building solid, calm businesses around very unsexy problems: invoicing for a niche industry, compliance workflows, scheduling in weird contexts, back-office tools nobody outside the niche has heard of.

I m curious whether your view of what s worth building has changed over the last few years. Would you be excited to build something deeply boring if the demand and willingness to pay were obvious? Or do you still feel pulled towards more visible, consumer-facing or hyped spaces? And for those already in boring niches, how has that choice played out in terms of users, stress and revenue?