Every morning starts the same way: open Techmeme, scan headlines, open too many tabs, and somehow end up 20 minutes deep into something you didn’t mean to read.
That loop is the problem. Instead of trying to “summarize the internet” or build another bloated AI dashboard, this project does something much simpler: take a strong source, rank and summarize it, and deliver a clean digest to Slack or Telegram.
That’s it—and that’s why it works.
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For years, founders have been fed the same comforting story:
“Building a startup is a marathon, not a sprint.”
It sounds wise. Mature. Sustainable.
It’s also mostly wrong.
If you’ve actually built something from zero—raised money, shipped under pressure, stared at a flat growth chart at 2am—you know the truth:
Startups don’t feel like marathons. They feel like repeated, borderline irresponsible sprints… with no clear finish line.
Marathons are predictable.
You train. You pace. You fuel. You suffer…
but in a controlled, linear way.
If you’ve done the work (in most cases), you’ll finish.
Startups?
Completely different game.
Calling it a marathon gives founders a false sense of control.
It suggests that if you just keep going steadily, things will work out.
They won’t.
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Let’s be honest.
Compliance today is broken for SMBs.
It’s fragmented.
Expensive.
Manual.
And worst of all—reactive.
You buy a few tools.
Hire a consultant.
Fill out some spreadsheets.
Panic before the audit.
Repeat next year.
Meanwhile, the reality has changed:
And one unsecured laptop can kill a deal.
Most companies treat compliance like documentation.
It’s not.
It’s continuous enforcement of controls across your entire environment.
That means:
You can’t fake that with PDFs.
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You don’t lose deals because your product is bad.
You lose them because someone in procurement asks: “Are you SOC 2 compliant?” — and you’re not.
That’s it.
Game over.
It is a security and trust standard. It proves that your company handles customer data responsibly across five areas:
It’s not a checklist.
It’s an audit.
An external firm comes in and validates that you’re not just saying you’re secure—you actually are.
SOC 2 isn’t about compliance.
It’s about trust at scale.
There’s been a lot of noise lately about productivity tools and the “perfect” engineering workflow.
Let’s slow down and separate what actually works from what just creates more overhead.
Here’s a boring truth: Slack is incredible for quick, ephemeral communication.
Here’s a less comfortable truth: It is an absolute nightmare as a system of record.
If you lead an engineering team or run a startup, you probably have a #daily-updates or #eod-reports channel.
The theory is sound.
Everyone drops a quick note at the end of the day: what they shipped, what blocked them, what’s next.
But here is what actually happens:
Those updates get posted.
Someone replies with an emoji.
A thread erupts about a weird bug in production.
Someone posts a picture of their dog.
By Friday, when you’re trying to answer a simple question—“What did we actually accomplish this week?”—those reports are buried under a mountain of noise.
You find yourself scrolling endlessly.
It’s exhausting.
And it doesn’t scale. Not to mention that if you will need SOC-2 (and you will 🙂 ) –> you can’t say “we have everything in Slack”
You could.
But engineers hate context-switching just to write a status update.
Slack is where the conversation is happening.
The friction to post there is zero.
The problem isn’t the input. The problem is the storage.
So I (=Gemini+Claude) built a bridge.
I got tired of losing track of momentum, so I wrote a bot that does the tracking for us.
It’s a lightweight NodeJS service that automatically extracts End-of-Day reports from Slack and structures them beautifully in a Notion database.
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The Murph Challenge isn’t a workout.
It’s a systems failure conducted at heart-rate redline.
If you’ve ever tried to remember whether you’re on rep 183 or 193 of squats while your lungs are filing a formal complaint, you already know: human memory is not a reliable datastore under load.
So I built a Murph tracker that does exactly one job well—count reps—while I focus on the important things, like not dying.
The Murph Challenge is performed on Memorial Day to honor Lt. Michael P. Murphy, a Navy SEAL killed in Afghanistan in 2005.
It was his favorite workout. Originally named “Body Armor”, which feels accurate in the same way “production incident” feels accurate.
The canonical version:
Optional difficulty modifier: wear a 20 lb vest and rethink your life choices.
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Every few weeks there’s a new take declaring that AI has made junior engineers obsolete, senior engineers redundant, and teams magically “10x.”
That story is lazy.
And dangerous.
AI didn’t remove the need for engineers. It exposed which parts of engineering were never that valuable to begin with.
What’s actually happening is a compression of execution. The typing, scaffolding, and boilerplate are cheaper than ever. Judgment, architecture, and responsibility are not. If anything, they’re more expensive—because the blast radius is larger.
This forces a reset. On roles. On metrics. On how we train people. On what “good” looks like.
Let’s talk about what to do.
If your juniors were hired to crank out CRUD and Stack Overflow glue, yes—AI just ate their lunch.
That’s your fault, not theirs.
Stop hiring “Keyboard Cowboys” –> Hire juniors who can:
Make AI usage explicit in job descriptions and interviews. Ask candidates how they validate AI output, not how they prompt it. The junior of the future is an operator and a critic, not a typist.
AI is great at producing answers.
It’s bad at knowing when they’re wrong.
Your review culture must check understanding, not just correctness. Ask:
Reward engineers who can debug, profile, and reason under failure.
That’s where AI still stumbles—and where real engineers earn their keep.
If AI tools are everywhere but governed nowhere, you already have a problem.
Standardize:
Ignoring this creates shadow-AI, silent leaks, and unverifiable decisions. You wouldn’t let people deploy random databases to prod.
Don’t do that with AI.
Output metrics are (now) meaningless. AI inflates them by design.
Measure what actually matters (DORA style):
AI can help you ship faster. It cannot guarantee outcomes. Your metrics should reflect that reality.
The future senior engineer doesn’t just write code. They design systems that coordinate intelligence.
Encourage work on:
This is the new leverage layer. Treat it as a core skill, not a side experiment.
Don’t flatten everyone into “full-stack generalists.”
You still need domain owners:
AI boosts breadth.
Humans anchor depth.
Lose that balance and your systems will rot quietly—until they fail loudly.
Assume new hires will use AI heavily from day one.
Onboarding should teach:
Otherwise you’re training people to copy confidently—and understand nothing.
Let AI scaffold, refactor, and generate tests.
Humans own:
If AI is making your design decisions, your team is already in trouble.
Always reproduce. Always measure. Always verify.
Treat AI like a fast junior engineer: helpful, confident, and occasionally very wrong. If you wouldn’t merge their code without checks, don’t do it for a model.
Code shows what the system does. It rarely shows why.
Write down:
This documentation becomes the truth source when AI generates plausible nonsense at scale.
Each engineer should expand into at least one adjacent area every year:
AI lowers the learning barrier. Use that advantage deliberately, or waste it.
Pick a core domain and become genuinely hard to replace there.
Depth is your moat. AI makes general knowledge cheap. It does not replace hard-earned intuition.
Hallucinations. Bias. Brittle reasoning. Silent errors.
Knowing failure modes is more valuable than knowing prompts. Engineers who understand where AI breaks will outlast those who just know how to ask nicely.
Portfolios beat resumes.
Show:
Real work cuts through hype instantly.
The future engineer isn’t judged by tasks completed.
They’re judged by how well the whole machine runs under stress.
AI compresses execution time.
It does not compress judgment, responsibility, or accountability.
Teams that double down on thinking, architecture, and learning will compound.
Teams that chase raw output will ship faster…
…straight into walls.
The choice is not whether to use AI.
The choice is whether you’re building engineers—or just accelerating mistakes.
For the past few years (2024, 2022, 2019, 2018, 2017, 2016, 2015, 2014, 2013), I’ve wrapped up the year by summarizing books and sports events—running, biking, gravel fun/suffering, and other questionable life choices.
2025 is no different.
Except it kind of is, because this was the year AI stopped being “the future” and the world become more (and more) crazy by the minute.
Let’s start with the books.
Clean Code – Robert C. Martin
Yes, I re-read it. Again. Apparently I still need to be reminded on many good aspects of ‘clean’ code.
Uncle Bob remains annoyingly correct.
Murakami – What I Talk About When I Talk About Running
I wrote about this one earlier this year. It’s not really about running. It’s about showing up, embracing boredom, and quietly grinding forward.
Which is also the most accurate description of debugging production on a Friday afternoon.
The Psychology of Human Misjudgment – Charlie Munger
I summarized Munger’s lessons this year. The man spent nearly a century documenting all the creative ways humans confidently shoot themselves in the foot.
Smart people don’t avoid mistakes—we just build better stories around them.
Range: Why Generalists Triumph in a Specialized World – David Epstein
Turns out being “kind of good at many things” isn’t a flaw—it’s a survival strategy. Epstein makes a compelling case that breadth wins in messy, unpredictable systems.
Which explains both modern tech careers and the contents of my garage.
Project Hail Mary – Andy Weir
A man, a spaceship, impossible physics problems, duct tape, and an alien who communicates via jazz hands and math.
Pure joy.
If The Martian made you happy, this one will make you irresponsible with sleep.
2025 was the year I finally admitted that gravel racing is just mountain biking for people who think they’re still road cyclists. 2025 was not about dabbling.
It was about distance, stubbornness, and rides long enough to require negotiations with your own legs. According to Strava, my idea of “a good day on the bike” is apparently anything north of 120 miles.
And next are the top 5 rides of the year, ranked by pure, unapologetic mileage:
166.8 miles · 8h05m · 4,350 m climbing
This was the big one.
Alpine County served up altitude, endless climbing, and the kind of fatigue that makes basic arithmetic difficult. Long, brutal, beautiful—and exactly as advertised.
Legs emptied.
Brain quiet. Highly recommended if you enjoy earning your recovery week.
161.5 miles · 5h32m · ~2,000 m climbing
Fast, flowy, and just enough climbing to keep things honest. One of those rides where everything clicks, the weather cooperates, and you start making wildly optimistic plans for the rest of the season. Dangerous mindset. Great day.
134.7 miles · 5h05m · ~1,500 m climbing
Long, steady, and sneaky-hard. The kind of ride that doesn’t feel epic until mile 110, when your legs quietly file a complaint. Classic endurance builder.
Zero regrets. Some soreness.
126.2 miles · 4h48m · ~1,850 m climbing
A greatest-hits tour of local suffering.
OLH never disappoints, Half Moon Bay always lies about the wind, and the ride home is where humility is restored.
Did this voluntarily just for a good espresso.
Would do it again.
121.2 miles · 5h19m · ~2,300 m climbing
Rolling climbs, long stretches of solitude, and enough elevation to remind you that “endurance ride” is just code for “extended negotiation with gravity.”
Strava confirms what I already suspected: 2025 was about volume, consistency, and seeing how far you can go before snacks become critical infrastructure.
The pain faded but the data remained.
I also finally nailed my race week taper strategy.
The secret is doing less while eating more.
Years of preparation paid off.
It started as “I’m tired of losing recipes in browser tabs” and escalated into a full-stack AI-powered cooking platform. React, Prisma, Node.js, OpenAI—and long philosophical debates with Cursor about database schemas at 1 a.m.
It now helps people manage recipes, generate new ones, and stop Googling “easy chicken recipe” for the 47th time.
My family uses it – so that’s already a win.
The pattern is clear: AI is incredibly useful—as long as you treat it like a very confident intern who occasionally hallucinates entire APIs.
I got strangely passionate about password security and MFA/passkeys this year. Mainly, after seeing some friends being hacked by some (really) bad actors. It’s far from being fun and with a few simple steps you can remove ~90% of the attackers.
The TL;DR:
* Turn on MFA.
* Use a password manager.
* Stop trusting your memory from 2014. Seeing “password123” still alive in 2025 does emotional damage.
My son asked, “Can we build something that counts our pull-ups?”
So we did. A real-time pull-up tracker using TensorFlow.js and a webcam.
Teaching a machine to recognize human suffering was harder than expected—but now we have data-driven trash talk.
Because if it’s not measured, did it even hurt?
2026 will probably look similar.
More books.
More miles.
More yelling at AI.
Definitely more coffee—especially since I wrote a guide on dialing in espresso.
If you made it this far, thanks for reading.
Here’s to another year of breaking things, building things, and occasionally fixing the things we broke.
Happy New Year 🥂 Be strong!
Big goals have a strange side effect: they make capable people behave like they’ve had too much coffee and not enough sleep.
You look at the size of the mountain, and suddenly you’re:
It feels productive. It’s not.
As the saying goes:
“You can’t cross a canyon in two jumps.”
Big goals don’t fail because they’re too big.
They fail because focus gets diluted.
Charlie Munger spent nearly a century studying how humans outsmart… themselves. The man treated bad decisions the way a forensic detective treats fingerprints. And the funny part? Most of the traps he identified hit smart people harder than everyone else. Intelligence doesn’t protect you—it just lets you come up with more elegant ways to be wrong.
Here’s the Munger playbook, rewritten in plain English and spiced with some real-world bruises. Ahh… it’s also much shorter then the original work. However, you do with to read the original as he is much better writer.
Let’s start with the elephant Munger kept in the room: brains aren’t the bottleneck—judgment is. You can have a rocket scientist mind and still steer straight into a mountain if you use it wrong.
When someone only uses the tools from their field, they distort reality to fit their toolbox.
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Ido Green 