Hacked Self exists because biohacking content is mostly supplement affiliates wearing a lab coat. We rank interventions by effect size - how much they actually move measurable outcomes - and the ranking barely changes:
Tier 1: boring, enormous effects
Sleep consistency (same time nightly) outperforms every supplement stack we've ever tested on HRV, resting heart rate, and cognitive scores. Not total hours - consistency. Social jetlag (weekends vs weekdays) shows up in bloodwork markers within weeks.
Tier 2: real but modest
Zone 2 cardio volume. Protein at breakfast. Morning light exposure. Each moves the needle a few points. Each requires weeks, not days, to show up in data - which is why nobody sells them.
Tier 3: the affiliate zone
Most powders, protocols with acronyms, and anything sold with a countdown timer. Some have studies; the effect sizes are a rounding error next to sleep consistency, and the price is 100x.
The method
Wearables plus quarterly bloodwork, changes one at a time, 8-week windows. It's slow and it's unglamorous, but it's the only way to know which of your interventions are doing anything. The data usually embarrasses your favorite habit. That's the point.
title: How we test 'buy it for life': two years of daily use before any recommendation
published: true
description: Most BIFL lists are affiliate pages. Our rule: nothing gets recommended before two years of daily use. Here's what two years reveals that two weeks doesn't.
tags: productivity, review
Most "buy it for life" lists are affiliate pages written by people who owned the product for a week. Durable Picks started because we kept re-buying the same category - bags, knives, cookware - on the strength of those lists.
The rule
Nothing gets recommended before two years of daily use. Not a review unit, not a stress test - daily use, by one of us, for two years. It's slow content and terrible for SEO, and it's the only standard that catches what actually breaks.
What two years reveals that two weeks doesn't
Hinges and fasteners fail at 8-14 months, right after return windows close. Nonstick coatings degrade on a curve you can't see in week one. "Lifetime warranty" splits into two groups: companies that honor it by mail in two weeks, and companies that require you to pay shipping to inspect a $30 item. We document the failure mode specifically - how the hinge breaks matters more than that it's guaranteed.
The surprise
Price correlated with durability up to about the 70th percentile, then disconnected completely. The most durable item in a category is almost never the most expensive one - it's usually the boring one without a marketing budget.
title: The 4% rule breaks at 35: withdrawal rates for a 50-year retirement
published: true
description: The 4% rule was designed for 30-year retirements. Retire at 35 and sequence risk in your first decade dominates everything. Here's the math that holds.
tags: finance, career
The 4% rule comes from the Trinity study, which modeled 30-year retirements. Retire at 65, fine. Retire at 35 and you're running a portfolio for 50+ years, where a different failure mode dominates: sequence-of-returns risk in your first decade.
Why the first decade eats people
A 1966-style retiree - bad early returns plus inflation - saw the 4% rule fail within 30 years. Stretch to 50 years and the tolerance for early disasters shrinks further. At Firenomics we model this with real historical sequences, and the pattern is consistent: it's rarely the average return that kills a plan, it's the order.
What actually helps (ranked)
- A flexible spending rule - guardrails that cut spending 10% after bad years beat any static rate.
- One year of cash spending outside the portfolio, refilled in good years.
- A variable initial rate: 3.3-3.8% for 50-year horizons instead of 4%.
- Earning any income, even trivial, which mechanically reduces sequence risk.
The uncomfortable summary
FIRE math is mostly about defense. The accumulation phase gets the blog posts; the withdrawal phase is where plans actually die. Model your first 10 years harder than everything else - that's where the risk lives.
title: 14 months to profitable: the side hustle timeline nobody publishes
published: true
description: My first profitable side project took 14 months. Here's the real dollar timeline by month, and the one metric that actually predicted success.
tags: business, startup
My first profitable side project took 14 months. Not 3 weeks, not "while watching Netflix." Fourteen months of evenings, with a real dollar timeline that looked like this:
- Months 1-4: $0. Building, no distribution. The classic mistake, inverted.
- Months 5-9: $30-120/mo. First users, mostly wrong users (freebie hunters).
- Months 10-13: $180-400/mo. Churn taught me more than acquisition did.
- Month 14: crossed $1,000/mo. Not life-changing, but real and repeatable.
I run Extra Hustles now, and the rule for every breakdown we publish: show the failure months. The flat early curve is where 90% of projects die, and pretending it doesn't exist is how "passive income" content stays profitable while its readers don't.
The one metric that predicted success
Not revenue. Not traffic. Months-to-first-stranger-payment - a stranger, not friends and family. Every project that got a stranger to pay inside 6 months eventually worked. Every project that couldn't, didn't, no matter how much I liked the idea.
Publish your timelines. The community's survivorship bias problem compounds when everyone only screenshots their best month.
title: Free AI-text detectors failed our paraphrase test. Here's the eval we use now
published: true
description: We benchmarked six free AI-text detectors against paraphrased output. The drops were brutal. Here's the three-suite eval harness that replaced them.
tags: ai, machinelearning
Before writing a single guide at Pastagi, we ran a humbling benchmark: six free AI-text detectors against progressively paraphrased model output.
The setup
Take 100 GPT-generated paragraphs. Pass each through a light paraphrase (synonym swaps, sentence reordering). Ask the detectors to classify both versions. No fine-tuning, no tricks - the same text with its surface scrambled.
The results that stung
Detectors that scored 90%+ on raw model output dropped 20-40 points after paraphrasing. One popular tool flipped to calling paraphrased human text "AI" more often than actual paraphrased AI text. The lesson isn't "detectors are bad" - it's that detector scores are meaningless without knowing the surface-form robustness of the specific detector.
What we do differently now
Every generation feature we ship gets an eval harness with three suites: raw output, paraphrased output, and adversarial near-duplicates. Regression means the harness fails, not vibes. And RAG answers must carry citations or they don't ship at all - attribution does more for trust than any detector.
The full benchmark methodology is on the site. Reproduce it before you buy any detector's enterprise tier.
title: Unlimited dev subscriptions: when flat-rate beats quotes (and when it doesn't)
published: true
description: The honest operator math on unlimited dev subscriptions: where the queue model wins, where it fails loudly, and the one question to ask before you subscribe.
tags: programming, business
Flat-rate "unlimited" dev subscriptions took heat over the last few years, mostly from people who never ran one. We run one at Poket Dev. Here's the honest math behind the model, from the operator side.
Where the model wins
The queue is the product. Clients who ship many small changes - landing pages, integrations, bug fixes, data cleanups - get enormous leverage, because the cost of asking for something drops to zero. No scoping call, no estimate, no approval cycle. The average request on our board takes under 4 hours of work, and that's exactly the workload the subscription handles efficiently.
Where it loses
Anyone selling you "unlimited" is really selling one-request-at-a-time. If you need a dedicated team grinding a 3-month build, subscribe to an agency. If you need 5 parallel workstreams, you need 5 subscriptions or a hire. The model breaks down loudly when clients expect parallel capacity from a serial queue, and honest operators say this up front.
The economics that make it work
The reason flat-rate survives: fixed estimates pad for unknowns, so clients overpay per-project by 20-40% to cover risk. Subscription pricing moves that risk to the operator, who can diversify it across a client base the way insurers do. We absorb a disastrous week here and there, and it's still cheaper for everyone than estimate theater.
If you're evaluating one of these services (ours or anyone's), ask exactly one question: what's the median turnaround on a small request? Everything else is marketing.
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