TravnikovDev

Modern solutions for a modern hiring mess

I was skeptical that “bots killed recruiting.” So I asked my n8n+ChatGPT researcher to dig. Apps per hire have roughly tripled since 2021 and auto-apply tools proudly shotgun “tailored” CVs. We don’t have platform-level proof that bots are the sole culprit, but the timing plus vendor capabilities… it’s not your imagination.

My take: stop grading words, start grading work. In an LLM world, text is makeup, not DNA.

What I’d do tomorrow:

• Add a 3–5 minute, role-specific micro-question as the gate. Rotate prompts. Turn off one-click apply. This single speed bump filters bots without punishing real people.
• Lead with a short, open-book work sample. Build a tiny feature, triage a ticket, write a brief. Score with a simple rubric. Decades of data say this predicts performance better than resume theater.
• Portfolios over claims. Real repos with commit history, source files, notebooks. Then ask “why did you choose X over Y?” Fakers crumble on the follow-up.

TravnikovDev

How to aim for 140 without fooling yourself

I got skeptical about the “we’ll live to 140 soon” headlines, so I asked my n8n+ChatGPT researcher-bot to pull the best evidence. The sober answer: today’s record is 122, most societies hit a soft ceiling in the 80s, and jumping to 140 would take multiple breakthroughs we do not have. That’s not pessimism. It’s math.

Here’s what actually moves the needle now. Think like a systems engineer: remove failure modes early. - Lower ApoB/LDL hard with statins/ezetimibe/PCSK9 if needed. The CRISPR one-shot LDL edits look exciting, but until we see 5-10 year outcomes, I’m not volunteering. - Keep BP near 120/80, vaccinate, screen for cancers on schedule. This is the boring 80% that prevents midlife “game over” screens.

Cardiorespiratory fitness is the closest thing to a universal upgrade. Each +1 MET maps to noticeably lower mortality. Translation: train your engine. I’m aiming for high CRF plus 2-3 days of strength so the chassis keeps up. Trade-off: push too hard, get in

TravnikovDev

Chinese AI models: my 2025 cheat sheet

I kept seeing Western models in every “top LLMs” list, so I asked my researcher bot to scan the Chinese stack. I wanted a practical map, not a press release. The result surprised me: this isn’t a sideshow anymore - it’s a toolkit.

Chat first. I’d start with DeepSeek for price-performance and reasoning, and Qwen2.5/3 when I want open roots, tooling, and stable docs. ERNIE and Hunyuan are strong inside their ecosystems; Zhipu’s GLM and Moonshot’s Kimi are solid consumer options. Caveat: ignore vendor “we beat everyone” slides and check LMSYS-type head-to-heads.

Images. For commercial work, Tongyi Wanxiang is my default - good taste for ecommerce and ads. ERNIE-ViLG 2.0 follows Chinese prompts well; CogView3 nails style and cultural nuance. Trade-off: most of these are API-only. If you need fully local and open, choice thins fast.

Video. Kuaishou Kling is creator-grade and actually used on platform - not just a sizzle reel. Vidu looks coherent and smooth; Hunyuan-Video

TravnikovDev

The viral population graph is wrong. The job puzzle isn’t.

I saw that chart claiming we jumped from under 5B to 8B people in five years and raised an eyebrow. I asked my ChatGPT researcher to sanity-check it. Result: we’re ~8.2B, growth is slowing, and the “explosion” isn’t the story. The story is aging in rich countries + a youth bulge in parts of Africa + AI reshaping tasks, not wiping out whole jobs.

Here’s the picture that actually changed my mind: the job ladder just lost the first rung while the roof got heavier. AI eats junior, routine tasks. Meanwhile, aging pushes huge demand for care, health, and skilled physical work that cannot be done through a browser.

Region lens that I now trust: high-income economies get chronic shortages in eldercare, trades, and energy retrofits. Middle-income is mixed - some aging, some young - with nearshoring and regulated operations as niches. Low-income has youthful cities and massive job-creation needs, with infrastructure and SMBs as the path if energy and governa

TravnikovDev

Stop being a generalist on Upwork. I wanted 3 small, weird niches that have jobs now and won’t be overcrowded by 2026.

So I asked my research bot to dig through demand signals, not hype. Here’s where I’d actually bet my time - and where I’d pass.

1. Privacy-grade conversion tracking and consent ops Think of it as fixing the plumbing behind your ads so it doesn’t leak under EU rules. Consent Mode v2, GTM server-side, Meta CAPI Gateway, TikTok Events API, plus sanity checks under Privacy Sandbox. The surprise: clients are posting for this, but few specialists exist. I’d sell tight starter packs: a 2-week Consent Mode retrofit, server-side tagging jumpstart, and clean CAPI/TikTok dedup. Hard boundary: I won’t do “consent workarounds.” This only works if the client agrees to honor consent and has a CMP. Best fit: EEA/UK ecommerce/lead gen on Google/Meta. Risk: APIs and policies will shift again in 2026.

2. LLM quality ops - evaluation and observability Teams shipped AI features; now they need them reliable and

TravnikovDev

Finish becomes addictive. That’s the point.

I came in skeptical about all the “dopamine discipline” advice, so I asked my ChatGPT researcher to pull the signal from the noise and ran it through my builder brain. Here’s what actually looks usable without turning your life into a productivity cult.

The bit that clicked: finish what you start. Completion is a tiny payout that teaches your brain effort leads to reward. It’s like closing a loop in code - the system calms down. Leave too many loops open and your attention keeps throwing interrupts. The surprise cost: if you quit halfway often, your brain learns avoidance pays. That habit compounds.

Where this works in practice: shipping a PR, sending a tough email, clearing one shelf, finishing the last rep. Where it fails: open-ended problems, exploratory research, early design. For those, premature “done” can kill good ideas. My filter - only force completion on scoped tasks with clear edges.

The “boring break” idea is underrated. Breaks are a volume knob. If

TravnikovDev

Turkey vs Chicken - who actually wins on sustainability and health?

I wondered about this the morning after Thanksgiving. So I asked my n8n+ChatGPT researcher to dig in, then sanity-checked the numbers myself. The verdict surprised me a bit.

On sustainability, chicken edges out turkey in most practical scenarios. Chickens grow faster, convert feed to meat slightly better, and need fewer resources per kilogram of edible protein. Think of them as compact, efficient hatchbacks. Turkeys are more like minivans - useful, but heavier to run. The catch I didn’t expect: a huge turkey that leads to leftovers you never eat can quietly double your footprint through food waste. That turns a close match into a clear loss.

Where this works in practice: standard chicken from efficient producers with good feed and ventilation. Where it fails or gets risky: small-scale or pasture-only birds. Lovely for welfare, but usually more land and higher emissions per kilo. Also, turkey has a longer grow-out period. If disease hits, t

TravnikovDev

Autophagy, starving, Ozempic: the internet’s new health triangle

I kept seeing posts worshipping autophagy like it’s a free car wash for your cells, so I asked my ChatGPT researcher to pull the signal from the noise. The theme surprised me: the biology is real, the claims are way ahead of the data.

Quick refresher: autophagy is your cell’s recycling bin. Low nutrients = your body takes out the trash. That part is solid. The leap to “starving prevents aging and cancer” is where it breaks.

Here’s the snag I didn’t expect: in humans we rarely measure autophagy directly - it’s invasive. Most “anti-aging/cancer” excitement comes from yeast, worms, mice. In people, fasting improves weight, insulin, maybe inflammation. That’s good. But “live longer and never get cancer”? Not proven.

Also, cancer is weird with autophagy. Early on, cleanup may reduce damage. Later, tumors can hijack autophagy to survive stress. So “more autophagy” is not a universal good - some trials even try blocking it in cancer therapy. That f

TravnikovDev

Pandora, but make it physics.

The Istanbul exhibit photos of James Cameron’s Hallelujah Mountains got stuck in my head. Could those cliffs really float? Curiosity won, so I asked my ChatGPT researcher to pull me back from movie magic to Maxwell.

Here’s the short version that changed my mind. Magnetic fields store pressure - think of an invisible air cushion. At 1 tesla you get roughly 0.4 MPa of “push.” Real force, not sci‑fi. Superconductors take it further: they kick fields out and, with flux pinning, lock themselves in place over magnets. That’s the famous hover that doesn’t wobble.

Diamagnetism is the quieter cousin. Everything resists magnetic fields a tiny bit. With absurdly strong fields and gradients, you can levitate a frog or a water droplet. It’s jaw‑dropping. It also needs lab magnets around 16 T. That’s MRI-on-steroids territory.

Now the trade-off I didn’t expect: scale kills the dream. A 1 km thick rock “raft” weighs about 26 MPa. To counter that with magnetic pressure, you are in the ballpa

TravnikovDev

Living on the edge of smartphone capabilities

I basically live with 2 GB free on my phone. That tiny buffer is my emotional support bar - the line between smooth and chaos.

I got curious why life on the last gigabyte feels so laggy, so I asked my ChatGPT researcher to dig in. The results made that storage bar look very different.

Modern phones write a lot of temporary stuff - caches, logs, thumbnails, updates. Flash storage can’t overwrite in place, it has to shuffle data first. When space is tight, the controller moves things around just to free a block. That busywork is garbage collection, and when the drive is packed, it turns every save into slow motion.

Here’s the picture that stuck: imagine editing a document on a desk covered in papers. To write one sentence, you first have to relocate piles. That’s write amplification - multiple internal writes to finish one user write.