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I built a daily tech digest that runs 100% on a Mac. Launching on Product Hunt today.

Hi IH, I'm David, IT security professional in Switzerland and solo founder of Brevio.news. It launches on Product Hunt today, so this is a good moment to share what I built and what I learned.

The problem
My own tech watch: RSS feeds, newsletters, YouTube channels, podcasts, academic papers. Reading everything took hours a week, skipping it meant missing things that matter in my job. I wanted one short email every morning at 9 AM, and I wanted to trust it.

What Brevio.news does
475+ sources across 7 themes, summarized into one daily email, in English, French, German, Spanish or Japanese. You pick your themes. Pro members also get a Sunday weekly edition, a private RSS feed and integrations with Obsidian and Readwise.

Three decisions that shaped the product

  1. Inference runs locally. Summaries are generated on a Mac with open models (Gemma, running on Apple silicon through MLX). No per-token bill, no reader data leaving the machine, and a cost structure that stays near zero as the source list grows. The trade-off: a full pipeline to build and a power cut that once took the whole thing down.

  2. A human reads every edition before it ships. AI summarization is cheap; trust is not. I review the edition each morning before the 9 AM send. This is the part I refuse to automate.

  3. Five languages from day one. Most of the digest market is English-only. Translation is part of the pipeline, not an afterthought, and Japanese pricing is in yen.

Stack, for those who ask
Python end to end: FastAPI, PostgreSQL, Redis, Celery on a small VPS behind Cloudflare, React frontend, Stripe for billing. The Mac does the heavy lifting and syncs to the VPS. Built evenings and weekends, with a lot of help from Claude Code for reviews and audits I would not have had time for alone.

Where it stands
Pre-revenue. Free, Starter and Pro plans are live. A founding member program opens to existing readers this week, publicly soon after.

What I would love from you
Honest feedback on the positioning (local AI plus human review: does it matter to you as a reader, or only to me as a builder?) and on the five-language bet. And if you have a minute, the Product Hunt page is here: [lien PH]

Happy to answer anything about the pipeline, the sources or the editorial routine.

on September 10, 2026
  1. 2

    Hey David! Massive respect for running the inference locally on a Mac using MLX and Gemma. That hybrid setup (local heavy-lifting for privacy/cost, VPS for delivery) is super smart.

    Quick question: How do you handle queue spikes or syncing between the Mac mini and your VPS if your home internet drops or power cuts happen? Wishing you the best on your PH launch today!

    1. 1

      Thank you, and good question, it is the part that keeps me honest. The split helps: the Mac generates and syncs ahead of time, the VPS holds the published content and sends the email, so the morning send does not depend on my flat being online at 9 AM. A power cut during generation costs me a cycle, not a delivery, as long as the sync happened before it.

      That said, the August outage taught me the failure mode is not the model, it is the process left in a half state: a scheduler that dies while a child process keeps a lock, a catch-up job restarting after the edition was already marked complete. Most of my reliability work since has gone into making those states detectable rather than into adding capacity. The honest summary is that I promise the 9 AM send, never the delivery, because SMTP is not mine to promise.

      1. 1

        Thanks for the awesome breakdown, David! Really appreciate the detailed look into your retry logic and failure modes.

  2. 2

    Running the summaries locally and still having a human review each edition feels like a thoughtful balance, especially for a digest people rely on for work. I’d be curious whether readers notice the difference more in fewer obviously wrong takes, or in the consistent editorial angle. The power-cut story also makes me think a small recovery/status path could be worth prioritizing early; reliability may become the strongest proof point alongside trust.

    1. 2

      Thank you, that is a fair way to put it. From what I see day to day, the human pass catches the small things rather than the big ones: a transcription that turned DeepSeek into "deep sea", a name spelled two ways in the same edition, a summary that drifts from what the source actually said. The seven editorials read fast in the morning, so I can go through them before the 9 AM send. I am still human, though, and some mistakes get past me; the goal is fewer of them, not zero.

      On the consistent angle, I think readers feel it over weeks more than in a single edition. That is my hope at least, I do not have enough data yet to prove it.

      You are right about reliability. The power cut moved it to the top of my list, and a proper recovery and status path is a good call. The one promise I make is the 9 AM send, so anything that protects it is worth prioritizing.

  3. 2

    The human-review claim seems more commercially important than the local AI architecture. Have existing readers shown willingness to pay specifically for that trust layer, or is the value proposition still being inferred from the product design?

    1. 2

      Honest answer: it is still inferred. I am pre-revenue, so nobody has paid specifically for the trust layer yet. What I have is qualitative: readers mention the errors they do not see rather than the review itself, which makes sense, a good review is invisible.

      The next test is a paid founding member offer I am preparing for existing readers first. If they pay, I will know the combination works; if they do not, I will learn whether it is the review, the price or the format. I will share the numbers either way.

      I agree with your framing: local inference matters to me as a builder for cost and privacy reasons, but readers buy trust and time saved. That is why the review is in the pitch and MLX is in the footnotes.

      1. 1

        That founding-member test is the right commercial signal. If you’re open to it, what’s the best email to reach you on?

        1. 1

          Thank you, and thanks for the questions, they helped me sharpen the answer. Happy to continue the conversation: the contact address on brevio.news reaches me directly, or send me a message on LinkedIn. Tell me a bit about what you have in mind and I will get back to you.

          1. 1

            Thanks! I’ve just sent it over.

            Looking forward to hearing your thoughts whenever you have a chance.

  4. 1

    I think the human pass is the product promise, not a safety net for the model. Track what you change during that pass: bad names, missing context, source drift, or tone. In DictaFlow, keeping the person's original meaning matters more than polishing the text. A simple review log could show which problems keep coming back. It would also give you better evidence of what readers trust you to catch.

    1. 1

      This reframing is right, and the review log is a good idea I do not have yet. Today my corrections live in my head and in the edition itself: bad entity names, a transcription that turned DeepSeek into "deep sea", a summary that drifts from what the source actually said, occasionally a tone that is more confident than the source warrants. Writing those down by category would tell me which failures are systematic and which are noise, and it would give me something honest to show readers instead of asking them to take the review on faith. Noted for the coming weeks, thank you.

  5. 1

    Congrats on shipping. The local-inference plus human-review combo is what stands out to me. As a reader, I think it matters, not because I'd notice the token cost, but because 'a human checks this before it ships' is exactly what makes me trust a digest instead of skimming it skeptically. I'm building an AI trade signal tool and ran into a similar tension: the AI scoring is the flashy part, but what actually earns trust is the boring layer around it, in your case a human review step, in mine risk controls and manual confirmation before anything executes. Good luck with the PH launch today, did you build any audience beforehand or mostly rely on launch day itself for traction?

    1. 1

      The "boring layer" framing is exactly it, and your parallel holds: nobody gets excited about manual confirmation before execution, but that is the part that decides whether someone trusts the system with something that matters.

      On the audience: almost none, and that is a deliberate cost. I built for close to a year before saying much, with a few dozen readers and no public presence. So launch day is not harvesting an audience, it is the first time I show the thing at all. It is the slower path and I would not recommend it, but I wanted the daily routine to be real before I asked anyone to rely on it. Good luck with the trade signal tool, the trust problem there is harder than mine.

  6. 1

    The Mac in your apartment worries me more than the local models do. You have already had one power cut take the whole thing down, and for a paid daily product a missed 9 AM send costs more trust than a slightly worse summary ever would, so keep a cloud fallback warm even if you never plan to use it. The human review is the opposite of a bottleneck, it is the product, and I would put it on the pricing page in those words.

    1. 1

      You are right to press on this, and you are right about the cost asymmetry: a missed send costs more trust than a mediocre summary. The architecture already separates the two, the Mac generates and syncs while the VPS holds and sends, so the 9 AM email does not depend on my flat being up at 9 AM. What a home outage costs me is a generation cycle, which is recoverable, rather than a delivery.

      A warm cloud fallback for generation is the honest next question. It cuts against the cost and privacy reasons I run locally, so I would rather first make the local side boring: detect the half states, recover without me, keep enough buffer that one lost night is invisible. If that is not enough, the fallback is the right answer and I will say so publicly.

      The pricing page point is well taken. It says the review happens; it does not say it in those words.

  7. 1

    The five languages are the part I would stress-test before pushing Pro. Are the French and German editions summarized from the sources directly, or translated from the English summary? Translated summaries drift twice, and one wrong claim in a reader own language kills trust in the whole 9 AM email. If each language is its own pipeline, your human pass just became five passes every morning.

    1. 1

      You put your finger on the real risk. The pipeline uses an English pivot: the summary is produced once from the sources, then translated, rather than five independent summarizations. So yes, the drift you describe is the failure mode I actually have to defend against, not a hypothetical one.

      What I do about it: fidelity gates that run mechanically on every translated edition (numbers, scales and separators per language, entity sequences, no raw English left in the Japanese), a per-language error metric I measure every morning against the English pivot of the day, and a spell and grammar pass on French, German and Spanish. Those gates are fail-closed, an edition that does not pass does not ship in that language. They catch mechanical drift reliably; they do not catch a translation that is fluent and subtly wrong, which is why I keep measuring rather than declaring it solved.

      And you are right that my human pass does not scale to five full readings every morning. It is honest to say the English and French editions get the most of my attention, and that the other three lean more on the gates and on periodic blind reads. That asymmetry is the thing I most want to fix before pushing Pro hard.

  8. 1

    Spot-on positioning, David. Tech curation is a crowded space, but your approach hits three distinct advantages:

    Obsidian & Readwise Integrations: For power users, the ability to pipe curated summaries directly into Personal Knowledge Management (PKM) systems is a massive conversion lever for the Pro tier.

    Human-in-the-loop: Don't downplay this! "AI generated, human verified" is becoming the gold standard for high-signal newsletters.

    Privacy: For IT security professionals, knowing raw data/feeds aren't being processed by random third-party APIs is a solid angle.

    How has the balance been between managing the daily 9 AM manual editorial review and your regular full-time job schedule?

    Upvoted on PH—excited to see Brevio grow!

    1. 1

      Thank you, and thanks for the upvote. The PKM angle is the one I underestimated the longest: readers who already live in Obsidian do not want another inbox, they want the material where their notes are.

      On the routine: the review happens early, before work. The seven editorials are short by design, which is what makes it possible at all, twenty to thirty minutes on a normal day. It works because the deadline is fixed and the format is bounded, not because I am disciplined. The honest trade-off is that Brevio gets my mornings, evenings and weekends, and that is sustainable for now but not a plan I would defend forever. Building for a way to keep the review real without it depending entirely on me is part of what the paid tiers are meant to fund.

  9. 1

    The constraint here isn’t collection, it’s attention: a digest that helps me decide what not to read is more valuable than one that simply compresses every source. I’d make the daily output end with a small queue—one item to read now, two to defer, and an explicit “skip” rationale—so the habit stays lightweight. Running locally on a Mac also seems like a good fit for keeping the feedback loop fast and private while you tune relevance.

    1. 1

      This is the most useful comment I have had since launch, thank you. You are right that the value is in what I help you not read. Today that job is shared between the theme selection (you only get the themes you chose) and the seven short editorials, which are meant to be the "read this, skip that" layer. What I do not have is your explicit queue: one to read now, two to defer, a stated reason to skip. I like it because it keeps the habit light and it is honest about the fact that most items are skippable. I am noting it for the next round of product work. If you subscribe and try it for a couple of weeks, I would be glad to hear whether the editorials already get you part of the way there.

  10. 1

    Congrats on the launch. The human review behind each edition feels like the stronger reader promise than the local-AI architecture itself, especially around a reliable 9 AM delivery.

    I’m testing a small tool that reviews public landing pages for clarity/conversion blockers using evidence from the page. I’d love to include brevio.news in a free beta review; in return, I’d only ask for 10 minutes of honest feedback on whether the diagnosis is useful. Interested?

    1. 1

      Thank you for the kind words. I am keeping my focus on readers and the editorial routine these next weeks, so I will pass on the beta for now. Good luck with the launch.

  11. 1

    You've identified something most builders miss: the measurement gap between what you built (local inference, human review) and what readers buy (trust and time saved).

    A good human review is invisible - readers only notice when something is missing. That makes it the hardest signal to charge for, because the metric that proves its value is the absence of bad outcomes. You can't sell based on mistakes you prevented; readers have no way to see the counterfactual.

    The founding member test solves for this by measuring actual behavior instead of trust claims. If they pay, you know the combination works. If they don't, the "why" becomes your real measurement system: is it price, format, or was the trust layer never as valuable as the pipeline implied? That second measurement - why they don't pay - is often worth more than knowing they do.

    1. 1

      Well put, and I agree with the last point most: the "why not" will teach me more than the "yes". I plan to ask it directly, one short message to every reader who opens the offer and does not take it, rather than guessing from the numbers. On the invisible review: the only way I have found to make it visible is to talk about the mistakes it catches, like a transcription that turned DeepSeek into "deep sea". Readers seem to trust the routine more when they can see it working. Thank you for taking the time.