TravnikovDev

Typing faster is not shipping faster. Your editor can finish your sentence - it will not fix your tests while you eat lunch.

My AI research agent sifted Google’s docs, InfoQ, and salty early user threads. Here’s the no-BS: Google Jules is a background coworker for code. You hand it a ticket - add tests, bump a dependency, clean up docs - and it disappears into a safe cloud box, then comes back with a pull request you can review and merge.

Plain English version: Jules clones your repo into a temporary Google Cloud VM, reads the code like a real teammate, drafts a plan you can edit, runs builds and tests, edits multiple files, collects logs and screenshots, and opens a PR. You also get an audio changelog so you can hear what changed without spelunking the diff. There’s a built-in critic that peers over its shoulder before the PR lands. It plugs into GitHub today, and you can drive it from the web, an API, or a simple CLI called Jules Tools.

This is not autocomplete. Autocomplete is a scooter - fun, fast, and

TravnikovDev

Stop chasing minute-long AI films - your feed only needs eight seconds.

Google’s Whisk Animate turns a single still into a short video with sound. You guide it with pictures - subject, scene, style - not prompt poetry. Click Animate, get a clean 8-second clip in 16:9 or 9:16. It feels like sketching, not engineering.

My AI research agent pulled the raw docs - Whisk’s Animate launched on Veo 2, while Google now pushes Veo 3 in Gemini and Flow with native audio. The Whisk UI never says which model, so assume Veo under the hood and likely upgraded. What you see is what matters - believable camera drift, smooth parallax, natural lighting, and sound baked in.

Why this is useful:

• Zero friction - your moodboard becomes motion.
• Fast - eight seconds renders quickly with synced ambience or sfx.
• Social-ready - landscape or vertical without hacks.

TravnikovDev

You can build an AI “model” for OnlyFans this month and still lose money by next month. The camera is cheap. The GPUs, data, and lawyers are the bill.

My AI research agent pulled the raw numbers, and the pattern is boring but real: video is the tax. The indie path that actually ships is not training from scratch. You pick a base model, fine-tune a tiny adapter for the face and vibe, then brute-force motion with short clips and heavy cleanup.

Plain English:

• Identity comes from a LoRA on a permissively licensed image model.
• Motion comes from an AnimateDiff-style pipeline in ComfyUI.
• Control comes from pose/depth guides. Stability comes from interpolation and upscaling. You babysit everything.

Hardware reality:

TravnikovDev

Hollywood isn’t getting replaced by AI. Your storyboard is. Flow - Google’s new AI filmmaking workspace - turns a one-line prompt into shots you can actually cut into a scene. Not a toy reel, real coverage.

My AI research agent pulled the docs and demos so you don’t have to. Here’s the plain-English version. Flow stitches Google’s models together: Veo for video, Imagen for images, Gemini for language. The latest engine, Veo 3.1, brings tighter story control, more believable motion and textures, and audio that finally feels synced instead of glued on.

What you get today: short cinematic beats. Think 4 to 8 seconds per generation, 720p or 1080p, widescreen 16:9 or vertical 9:16. You can prompt camera moves - push-in, handheld tracking, dolly - pick a style, and feed up to three reference images so your lead doesn’t morph between shots. There’s a scene builder, first-frame to last-frame transitions, and a simple way to extend a moment without wrecking continuity.

How to actually use it: write a one-paragraph

TravnikovDev

Hot take: Gmail isn’t secretly feeding Gemini your private emails. The scary posts made for great clicks - the boring truth didn’t.

My AI research agent pulled the receipts from Google’s Workspace privacy hub, plus Snopes and Malwarebytes’ corrections. Here’s the plain-English version.

• No, there was no automatic opt-in that let Google train generative AI on your inbox. For business/edu accounts, Google says your content isn’t used for gen-AI training without permission. Consumer Gmail got swept into a rumor in late 2025 that fact-checks knocked down. Nobody flipped your settings behind your back.
• Yes, Gmail reads message content to run long-standing features. That’s spam filtering, Smart Compose/Reply, category tabs, and those helpful summary cards. That processing powers the product - it is not the same as training a giant text generator on your private emails.
• Turning off Smart features stops those conveniences, not an AI training pipeline. You can still use Gmail just fine.
• The spell-check scare

TravnikovDev

You don’t need a Delaware fairy godmother to raise venture. In most emerging hubs, money moves on local rails - and it works.

I had my AI research agent pull how early rounds actually close across India, SEA, Africa, and LatAm. The pattern is repeatable: same economic logic as the US, but local paperwork, local regulators, local exit paths.

What this really looks like

• Early money uses straight equity, convertible notes, SAFE-like docs, and more recently revenue-based financing. Terms rhyme with Silicon Valley, but the wrappers are local. Typical priced seed or Series A sells about 10-30 percent. Exits are mostly acquisitions or secondaries. IPOs are a nice poster, not your plan.

Regional flavors

• India - priced rounds use CCPS; pre-seed often iSAFE that converts into CCPS. Foreign convertibles allowed with RBI filings. SEBI AIFs write many of the checks.
• Singapore/SEA - VIMA templates standardize term sheets and a CARE convertible. Ops often sit in Indonesia or Vietnam, but governance stays under Sing

TravnikovDev

Your post goes live and strangers like it in 60 seconds. No, you’re not special - the machine is testing you.

I dug in with my AI research agent and the receipts are clear 🕵️ - auto-likes and auto-comments exist, they’re banned, and they still slip through because some of it looks human and the detection isn’t perfect.

First, why those instant non-follower impressions happen at all - LinkedIn test-distributes every new post to a small circle beyond your followers. If people pause or comment, it widens the pipe fast. LinkedIn’s own engineers have written about this - dwell time matters: https://engineering.linkedin.com/blog/2020/improving-feed-relevance-with-dwell-time

How the fake gravy gets poured on top:

• Engagement pods - groups coordinate early likes/comments to juice reach. Think quick, clustered reactions, often with generic comments. Primer and pros-cons here: https://www.podawaa.com/blog/linkedin-engagement-pods and here: https://expandi.io/blog/linkedin-engagement-pods/
• Automation suites with A

TravnikovDev

If GitHub Gists could juice rankings, every casino and crypto site would be #1 by lunch. They’re not. And there’s a reason.

I had my AI research agent pull the raw signals on this, and the pattern is boring in the best way: gists get indexed, links get neutered, and spam gets ignored.

How it actually works:

• Gists are indexable. You can find them in Google. But they mostly show up for developer queries, code snippets, and very specific technical stuff.
• Links inside gists are almost always tagged nofollow/ugc. Translation: Google treats them as hints, not votes. You might get discovery. You won’t get trust.
• Gists rarely hold page-1 real estate for commercial keywords. For most niches, they’re background noise.

How spammers use it:

TravnikovDev

Stop waiting for your third startup. VC doesn’t reward scar count - it rewards speed, traction, and problems that burn cash to beat the clock.

2025 is not 2023. The market is pickier but alive. Global funding climbed back above $300B in 2024 and Q2/Q3 this year landed near $90-100B each, with AI drinking from the biggest hose. Small US rounds fell to roughly half of deals - the bar rose, not vanished.

I had my AI research agent pull NVCA, Carta, Crunchbase, Stanford - the numbers don’t back the “third-time” fairytale. Most companies start bootstrapped, and most never raise at all. Prior success helps a bit, sure, but there’s no magic unlock at startup three.

What VC is actually for: gasoline on a working fire when time-to-market, network effects, or capex outrun bootstrapping. Not life support.

Early-stage reality check:

• US seed is roughly $2-3M on about $15-16M pre-money.

TravnikovDev

A new type of profession is developing: AI tutors for models.

Here’s the twist - if you’ve ever nitpicked a code review or triaged a messy bug queue, you already qualify. You literally get paid to teach robots how to behave.

I had my AI research agent pull the raw data across Reddit worker threads and platform pages. The signal is noisy, but the pattern is clear: RLHF work is a real side hustle with real money for people who can write, reason, or code.

Top 10 platforms not on the usual list

• Outlier AI - expert RLHF across writing, math, code. High activity, best pay.
• DataAnnotation.tech - evals for writing, safety, coding. High activity, selective onboarding.
• Amazon MTurk - find AI eval, ranking, response rating. Always on, quality varies.