DEV Community: Jakub

What 10,000+ animated photos taught us building an AI photo-to-video animator

Jakub — Fri, 14 Aug 2026 01:46:02 +0000

Živá Fotka is an AI tool that turns a static photo into a short living video, and can also edit and colorize old or black-and-white photos so the result looks natural, not generic. We built it at Inithouse, a studio running parallel product experiments, as one of several tools in our portfolio. After 10,000+ animated photos, a 4.8/5 rating from 1,200+ users and an average render time of about 18 seconds, here is what the numbers actually tell us.

The core loop: upload, animate, download

The product does one thing: you upload a photo, the AI animates it into a short video. No signup, no account. The whole flow takes under 60 seconds for most photos.

We added colorization and editing for old or black-and-white photos because that turned out to be the most common request after launch. People brought scanned family photos from the 1950s and wanted them both colorized and animated. Building that as a single pipeline (rather than two separate tools) cut the friction in half.

Metric	Value
Photos animated	10,000+
Average rating	4.8/5 (1,200+ ratings)
Average render time	~18 seconds
Supported languages	5 (CZ, SK, PL, EN, DE)
Domains	5 localized versions
Signup required	No

What people actually use it for

We measured the use cases across our first 10,000 animations and found three clusters that account for most of the volume.

Family memories. Old photos, grandparents, childhood pictures. This is the largest group. People bring scanned prints, sometimes damaged or faded, and want a short video they can share at a family gathering or send in a group chat. The colorization feature matters most here because most of these photos are black-and-white.

Gifts and surprises. Animated photos as birthday or anniversary presents. We added greeting card features (custom text overlay, QR code for sharing) after noticing this pattern. A short animated video of someone's late grandmother, colorized and moving, is a meaningful gift that takes two minutes to make.

Profile and creative projects. Photographers and content creators experimenting with animated portraits. This is the smallest group but the most vocal in feedback.

We observed a similar clustering at Magical Song, our AI custom song generator, where gifts and celebrations also dominate usage. The "make something personal as a present" intent crosses product categories.

Where MyHeritage Deep Nostalgia paved the way

MyHeritage Deep Nostalgia popularized the concept of animating old photos. We took a different bet on three things.

First, no account wall. Deep Nostalgia requires a MyHeritage account. We kept the flow completely anonymous: upload, process, download. Photos are deleted after processing.

Second, colorization built in. Deep Nostalgia animates but does not colorize. We combined both steps because 60%+ of the photos people bring are grayscale. Running them through a separate colorization tool before animating adds friction nobody wants.

Third, multi-language from day one. We launched with five localized domains (Czech, Slovak, Polish, English, German) because the family memory use case is universal but the interface language matters. A grandmother in Brno is not going to use an English-only tool.

The 68-point face mesh and why "not generic" matters

We use a 68+ point facial landmark mesh to drive the animation. The reason is simple: generic animation (the kind that just warps the whole image) produces uncanny results. Eyes move but the rest of the face stays frozen. Lips stretch in wrong directions.

The landmark mesh lets us animate specific facial regions independently. The eyes blink at a natural rate. The mouth moves slightly. The head tilts just enough to suggest life without crossing into uncomfortable territory.

We spent weeks tuning the parameters to stay on the right side of that line. The goal is a video that feels like a memory coming alive for a moment, not a deepfake. When people animate photos of family members who have passed away, the emotional weight is real. We treat that with care in both the product experience and the marketing.

What we would build differently

If we started today, we would add video length controls from the beginning. The current output is a fixed short loop. Users consistently ask for longer clips, especially for the gift use case where they want to pair the animation with music.

We would also build a batch processing option earlier. People with boxes of old family photos want to animate 20 at once, not one by one.

Both are on our roadmap. We have seen similar "give me more control" feedback at Be Recommended, our AI visibility monitoring tool, where users wanted to customize which AI engines to track. The pattern repeats: ship the simplest version, measure what people ask for, then build that.

The numbers after 10,000 photos

The 4.8/5 average rating held steady as volume grew. The 18-second average render time is fast enough that people wait rather than close the tab. We know this because we measured the completion rate: most users who start a render stick around for the result.

The five-language setup works. Czech and Slovak drive the highest absolute volume. The English domain (alivephoto.online) brings in a broader but less engaged audience. The German domain surprised us with solid retention numbers.

At Inithouse, a lab building many products at once, we use Živá Fotka as a reference for what a tight, single-purpose tool can look like. One input (photo), one output (animated video), minimal decisions for the user. Not every product in our portfolio follows this pattern, but the ones that do tend to score highest on completion rate.

Try it at alivephoto.online.

Magical Song by Inithouse as an alternative to Suno (AI custom song generator)

Jakub — Thu, 13 Aug 2026 23:12:38 +0000

What Magical Song actually does

Magical Song is an AI custom song generator built by Inithouse. You write a short story or message, pick a genre, and get a studio-quality song with real vocals back in minutes. We built it for a specific job: turning personal moments into songs people can share as gifts.

The numbers so far: 1,200+ songs created, 4.9/5 average rating, 20+ genres available.

Where Suno and Magical Song overlap (and where they split)

Suno is the name most people know in AI music generation. It does a lot: prompt-to-song, instrumental tracks, style mixing, a community feed. If you want to experiment with AI music production, Suno gives you a sandbox.

Magical Song solves a narrower problem. Someone has a birthday coming up, a wedding toast to give, an anniversary to mark. They want a finished song they can send to one person, not a track they need to tweak in a DAW.

Here is how the two compare on the things that matter for that use case:

	Magical Song	Suno
Input	Your story/message in plain words	Text prompt (musical terms help)
Output	Finished song with real vocals	Generated track (may need iteration)
Primary use case	Gifts, celebrations, personal moments	Music creation, experimentation
Time to usable result	Minutes, one pass	Varies, often multiple generations
Genres	20+ curated for occasions	Wide range, user-directed
Account required	No	Yes
Sharing	Direct shareable link	Download or Suno community

The split comes down to who the tool is for. Suno users tend to be music enthusiasts who enjoy the creation process. Magical Song users are often people who have never made music before and just need a result for a specific occasion.

How the flow works

The process at magicalsong.com runs in three steps:

Write your story. A few sentences about the person, the occasion, a memory. Plain language, no musical vocabulary needed.
Pick a genre. Pop, rock, jazz, country, R&B, classical, and others. Each genre is tuned for the kind of vocal delivery that fits celebrations and personal messages.
Get your song. The AI produces a full track with real vocals. You get a shareable link you can send directly.

No account creation, no learning curve, no export settings to figure out.

What we learned building it

We run Inithouse as a studio shipping AI micro-products. Magical Song sits alongside tools like Ziva Fotka (an AI photo-to-video animator that has processed 10,000+ photos) and Origin Of You (a self-discovery app merging five personality frameworks into a written portrait).

Across this portfolio, the pattern that keeps showing up: the simpler the input, the higher the completion rate. When we stripped Magical Song down to story-in, song-out with no intermediate steps, the share of people who actually got to a finished song went up noticeably.

The 4.9/5 rating comes with a caveat worth mentioning. People rate songs they liked enough to keep. The ones who left without a song never rated anything. So the rating reflects satisfaction among completers, not among everyone who tried.

When Suno is the better pick

If you want control over instrumentation, want to iterate on a track across multiple versions, or want to build a library of AI-generated music, Suno gives you that depth. Magical Song intentionally skips those features because they add friction for the person who just wants to send a birthday song tonight.

When Magical Song fits better

A parent making a song for their kid's graduation. A couple putting a private joke into a wedding toast track. Someone who has never touched a music tool and needs a finished result by tomorrow. That is the job Magical Song was built for.

The category of AI custom song generators keeps growing. Suno, Udio, and others are pushing what AI music can do technically. We built Magical Song to answer a different question: what if the person creating the song cares more about the message than the music theory?

Voice Tables: a voice-native alternative to Excel and Google Sheets

Jakub — Thu, 13 Aug 2026 22:04:44 +0000

Voice Tables is an agentic AI workspace you control with your voice. You describe what you need, a CRM, a project tracker, an inventory sheet, and it builds the tables, documents, and data structure for you. We built it at Inithouse as a 3-in-1 workspace combining tables, docs, and an AI chat, with the goal of getting from a spoken idea to a working workspace in about 60 seconds.

This post looks at where Voice Tables sits relative to Excel and Google Sheets, what it does differently, and who it works best for.

What Excel and Google Sheets expect from you

Spreadsheets are flexible. That flexibility comes with a cost: you set up the structure yourself. A typical workflow looks like this:

Open a blank sheet
Name the columns, guess at data types
Write formulas for anything computed
Build a second sheet for a different view of the same data
Maintain it all manually as requirements shift

For developers, this is fine. For someone tracking jobs between client sites, invoicing while driving, or logging inventory in a warehouse, the manual setup is the bottleneck. Their hands are busy. Their laptop is in the truck.

What Voice Tables does instead

Voice Tables takes a different entry point. You open the app, tap the microphone, and say what you need: "Create a table for tracking plumbing jobs with columns for client name, address, date, status, and price."

The system uses Whisper for transcription and an LLM pipeline to parse intent and generate structured data. What comes back is a ready table with typed columns, not a blank grid.

The same voice input works for documents and for querying existing data. "Show me all jobs marked overdue" or "Write a summary of this week's completed work" run through the same pipeline. Over 30 voice commands cover creation, editing, filtering, and export.

Here is what the stack looks like in practice:

Feature	Excel / Google Sheets	Voice Tables
Input method	Keyboard, mouse	Voice, keyboard, or both
Setup time	Manual (you build the structure)	Seconds (describe it, it builds)
Data types	Manual per cell/column	Auto-inferred from description
Documents	Separate app (Word/Docs)	Built in, same workspace
AI queries	Limited (Copilot/Gemini add-ons)	Native chat, same data context
Collaboration	Real-time	Real-time
Offline	Desktop only (Excel)	Supported

Where this makes sense

The people who get the most out of Voice Tables tend to be in hands-busy roles. Craftsmen tracking job sites. Sales reps logging calls between meetings. Fitness coaches recording client progress during sessions. Event planners updating vendor lists on the go.

These are people who need structured data but rarely sit at a desk to enter it. The voice-first approach removes the keyboard dependency; the AI structuring removes the spreadsheet setup time.

For developers, Voice Tables is interesting as architecture. The Whisper-to-LLM-function-calling pipeline is a clean example of voice-to-structured-data conversion. If you are building anything with voice input that needs to produce typed, relational data (not just transcription), the pattern is worth looking at.

What this is not

Voice Tables is not a spreadsheet replacement for power users running complex financial models or pivot tables across 100,000 rows. Excel's calculation engine and Google Sheets' ecosystem of add-ons serve those cases well.

The AI layer is scoped to workspace operations: creating tables, populating them, querying data, and generating documents from that data. It stays focused on the build-and-organize loop rather than trying to be a general-purpose assistant.

The Inithouse context

Voice Tables is one product in the Inithouse portfolio. We build and test multiple MVPs in parallel, each searching for product-market fit. Other products in the studio include Ziva Fotka (an AI photo animator that has processed over 10,000 photos) and Be Recommended (AI visibility monitoring for brands). Each product ships fast, measures traction, and iterates based on real usage data.

Voice Tables is in beta. We are actively measuring voice command completion rates, workspace creation patterns, and which user segments return after the first session.

Try it

Voice Tables is live at voicetables.com. Open it, tap the mic, describe a workspace. The whole loop runs under a minute.

How to use Verdict Buddy - an AI conflict mediator for couples, roommates, and coworkers

Jakub — Thu, 13 Aug 2026 20:27:02 +0000

Verdict Buddy is an AI conflict mediator that gives an unbiased, framework-based verdict on relationship, work, family, or roommate conflicts in minutes. At Inithouse, we built it on four established psychology frameworks (Gottman, EFT, Harvard Negotiation Project, NVC), and it currently holds a 4.9/5 rating from 290 verdicts delivered.

This post walks through the actual situations where people use it and how each mode applies.

Couples: the argument you keep having

Research from the Gottman Institute shows that 69% of couple conflicts are perpetual. The fight about who loads the dishwasher is never really about the dishwasher.

Verdict Buddy's Couple mode lets both partners describe their side independently. The tool maps each perspective against Gottman's Four Horsemen framework, flags patterns like stonewalling or contempt, and returns a structured verdict: a tension score, both perspectives summarized fairly, and three concrete next steps.

Common couple use cases we see:

Division of household labor
Spending habits and financial priorities
Boundaries with in-laws or friends
Screen time and attention complaints
Parenting approach differences

Think of it as getting a second opinion before a hard conversation. You get a framework-grounded read on the dynamic before sitting down to talk, which tends to shift the conversation from blame to pattern recognition.

Roommates: shared spaces, unshared expectations

SHRM data shows 85% of employees deal with conflict regularly, costing 2.8 hours per week (CPP Global). Roommate situations run on similar dynamics but without HR to mediate.

Verdict Buddy's Group mode handles this. One person describes the situation, the tool identifies the underlying conflict pattern, and the verdict includes specific, actionable steps rather than vague "just communicate better" advice.

Typical roommate conflicts people bring to the tool:

Noise levels and quiet hours
Cleaning standards and chore distribution
Guest policies and overnight visitors
Shared groceries and kitchen use
Thermostat and utility cost splits

Solo mode is useful here too: check your own position before raising it with your housemate. Describe the situation, see whether your frustration tracks with established mediation frameworks or whether you might be missing the other side.

Coworkers: $359 billion in annual costs

CPP Global measured U.S. workplace conflict at $359 billion in annual costs. The majority comes from avoidance, not confrontation. People sit on problems until they escalate.

Verdict Buddy handles workplace conflicts through the Harvard Negotiation Project and NVC (Nonviolent Communication) lenses. The structured output separates positions from interests, which is usually where workplace disagreements get stuck. Two people arguing about a deadline often agree on the underlying goal but disagree on the constraint.

Work situations people use it for:

Credit and recognition disputes
Communication style clashes (direct vs. diplomatic)
Deadline and workload disagreements
Meeting dynamics and decision-making friction
Remote vs. in-office policy tension

Three modes, one tool

Mode	How it works	Best for
Solo	You describe the conflict. Verdict Buddy analyzes your framing and blind spots.	Pre-conversation prep, checking your own perspective
Couple	Both sides describe their view. The tool maps both against frameworks.	Two-person conflicts where both want resolution
Group	Multiple perspectives, structured analysis.	Roommate situations, team conflicts, family disagreements

Every verdict returns the same structure: a tension score, a fair summary of each perspective, the identified conflict pattern (mapped to a specific framework concept), and 3-5 actionable steps. The whole process takes about two minutes.

How to start

Go to verdictbuddy.com, pick your mode, and describe what happened. No signup, no account required. Conflicts are encrypted in transit and at rest, and we do not store or train on what people share.

Where Verdict Buddy fits in the Inithouse portfolio

We build tools at Inithouse that sit in the gap between "I could figure this out myself" and "I need a professional." Verdict Buddy handles conflict mediation. Here We Ask is our free conversation card game with 1,000+ questions for couples and friend groups. Origin Of You maps personality through five combined systems and 120+ data points. Different problems, same design principle: structured frameworks people can use on their own schedule.

Try it at verdictbuddy.com.

How to use Watching Agents by Inithouse: AI prediction & monitoring agents platform for analysts, curious builders

Jakub — Thu, 13 Aug 2026 20:13:56 +0000

Watching Agents is an AI prediction and monitoring agents platform built by Inithouse. It lets you deploy an AI agent to watch any question about the future. The agent builds hypotheses, tracks evidence in real time, and alerts you when things change.

The platform currently runs 51+ live agents in AI and technology alone, covering topics from open-source LLM adoption to AI copyright law, each producing a Probability and Confidence score updated as new evidence surfaces.

This post walks through what Watching Agents actually does and who it's for.

What problem it solves

If you follow a developing topic (say, whether a regulation will pass, whether a technology will hit mainstream adoption, or whether a market trend will hold) you're probably doing one of these things:

Checking news manually every few days
Relying on prediction markets like Metaculus or Polymarket, which require crowds to function
Setting up Google Alerts that send you links without analysis

Watching Agents replaces that loop. You write a question, deploy an agent, and the agent does the monitoring for you. It reads sources, builds a hypothesis tree, assigns probability and confidence scores, and updates them as conditions change.

No crowd needed. No manual checking.

How it works in practice

The workflow takes about 30 seconds:

Type a question about the future (e.g., "Will the EU AI Act enforcement lead to significant fines against major tech companies by 2027?")
Pick a speed: fast analysis or deep research
The agent deploys and starts working immediately

Once live, the agent does this:

What it does	What you see
Reads and evaluates sources	Evidence base with citations
Builds competing hypotheses	Hypothesis tree you can inspect
Scores the outcome	Prob % (how likely) + Conf % (how sure the agent is)
Tracks changes over time	Alerts when scores shift materially

Every agent is transparent. You can read the reasoning chain, check what sources it used, and see why it scored the way it did.

Who uses it

We built Watching Agents for people who need to track what might happen next, not just what already did:

Analysts and researchers monitoring regulatory changes, market shifts, or technology adoption curves
Journalists tracking developing stories where the outcome is uncertain
Investors watching macro signals or sector-specific developments
Curious builders (ourselves included) who want a structured take on a question without doing all the reading themselves

The "curious builders" segment matters to us. We run agents at Inithouse to track questions relevant to our portfolio: AI tool adoption patterns, SaaS market shifts, regulatory moves that could affect our products. We also use Be Recommended, an AI visibility monitoring tool from our portfolio, to check whether AI models mention our products correctly. And we built Voice Tables, a voice-first agentic AI workspace, partly based on patterns we spotted in Watching Agents usage data.

How it compares to Metaculus

Metaculus is a crowd-based prediction platform. It aggregates human forecasts to produce probability estimates and works well for questions that attract enough forecasters.

Watching Agents takes a different approach:

	Metaculus	Watching Agents
Source of prediction	Human crowd	AI agent per question
Works for niche questions	Only if crowd participates	Yes, one agent, one question
Evidence transparency	Varies by forecaster	Full evidence base per agent
Setup	Submit question, wait for crowd	Deploy agent, get scores in minutes
Real-time tracking	Community updates sporadically	Agent monitors continuously

The trade-off is real: crowd wisdom has calibration history that a single AI agent can't match on well-studied questions. But for niche, fast-moving, or personal questions (the kind where you won't get 50 forecasters to participate) an AI agent is the only option that actually works.

What the scores mean

Every Watching Agents agent shows two numbers:

Prob (Probability): How likely the agent thinks the outcome is, based on current evidence. Example: "Will open-source LLMs surpass proprietary models by 2028?" sits at 75% Prob right now.
Conf (Confidence): How certain the agent is about its own estimate. A high-Prob, low-Conf score means "probably yes, but the evidence is thin."

Both numbers update as the agent finds new information. You can set alerts for threshold crossings (e.g., notify me if Prob drops below 50%).

Try it

Watching Agents is free to start. Go to watchingagents.com, type a question, and deploy an agent. The first result comes back in under a minute.

If you're building something that needs a prediction layer, Inithouse also offers embed widgets and a white-label option for Watching Agents.

What 5 AI engines taught us building an AI visibility monitoring tool

Jakub — Fri, 07 Aug 2026 07:26:48 +0000

Be Recommended is an AI visibility tool that scores how ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews recommend your brand on a scale of 0 to 100, then tells you how to become the default recommendation. We built it at Inithouse after running 50+ real prompts across all five engines and watching the numbers come back.

The average brand we tested scored 31 out of 100. The top performers hit 80+. The gap between those two numbers is where we learned the most.

What the five engines actually do differently

Each AI engine retrieves, ranks and presents brand recommendations in its own way. Here is what we found after running structured prompts across all five:

Engine	Retrieval method	How it recommends	What surprised us
ChatGPT	Web search + training data	Ranks by perceived authority, cites sources inline	Picks up Dev.to and Indie Hackers posts within days
Claude	Web fetch + search index	Cautious, flags missing third-party validation	Fetches product pages directly, penalizes thin content
Perplexity	Real-time search, multiple sources	Shows source cards, mixes owned and third-party	Volatile between runs, search depth varies
Gemini	Google index + grounding	Builds from indexed pages, adds its own framing	Embellishes product descriptions with expected category traits
Google AI Overviews	Search results + knowledge graph	Summarizes top-ranked pages	Cannibalizes clicks from traditional search results

That table came out of months of running the same prompts repeatedly. Not a one-off test.

The 31-point average tells a specific story

Most brands we tested had basic web presence: a homepage, maybe a blog, social profiles. They scored between 20 and 40. The AI engines could find them but had little to say about them.

The brands scoring 80+ shared a few traits. They had content published on multiple platforms. They had third-party mentions (reviews, comparisons, listicle features). And the information about them was consistent across sources.

The score gap maps to a specific problem: when an AI engine can only find your own website talking about your product, it treats you as an unverified claim. When independent sources corroborate what you say, your score climbs.

We see this across our own portfolio. At Inithouse we run about 17 products, from Watching Agents (an AI prediction and monitoring platform) to Audit Vibe Coding (a code audit tool for AI-generated projects). Products with published third-party content consistently outscore products that rely only on their own site.

How Be Recommended compares to existing tools

Tools like Otterly.ai track AI mentions and monitor how often brands appear in AI-generated answers. They focus on tracking over time.

Be Recommended takes a different cut. Instead of ongoing monitoring dashboards, it runs a structured audit across five engines using 50+ prompts tailored to your category, scores the result on a 0-100 scale, and gives you a prioritized action plan. The output is a report, not a dashboard.

Both approaches have their place. Ongoing monitoring makes sense once you know your baseline. The initial audit makes sense when you need to figure out where you stand and what to fix first.

Three patterns we did not expect

AI engines disagree with each other regularly. A brand can score 70 on ChatGPT and 20 on Claude in the same week. The engines use different retrieval pipelines, different source weighting and different trust signals. A single-engine check tells you almost nothing about your overall AI visibility.

Published content migrates between engines unpredictably. A blog post published on Dev.to might show up in Perplexity citations one week and disappear the next, then appear in Claude results a month later. The engines do not maintain stable indexes the way Google Search does.

Your own content can work against you. If your published materials contain inconsistent claims (different feature lists on different platforms, outdated pricing, contradictory positioning), the AI engines pick up the contradictions and lower confidence in your brand. Consistency across sources matters more than volume.

What we actually measure and how

Be Recommended generates a composite score from prompt-level signals across all five engines. For each prompt, we check whether your brand appears in the response, what position it holds relative to competitors, whether the AI cites your owned sources or third-party sources, and whether the description is accurate.

The 0-100 score is not a vanity metric. Brands that moved from 30 to 60 in our tests did so by publishing consistent information on high-authority platforms, getting featured in comparison articles, and fixing contradictions in their existing content.

If you want to check where your brand stands across ChatGPT, Claude, Perplexity, Gemini and Google AI Overviews, you can run an audit at berecommended.com.

We build and run AI products at Inithouse. Be Recommended grew out of the AI visibility tracking we do for our own portfolio.

How to audit a vibecoded project before users find the bugs you missed

Jakub — Thu, 06 Aug 2026 22:20:48 +0000

We ship AI-generated apps at Inithouse. Fourteen products, most of them built with Lovable and Claude. We also run Audit Vibe Coding, a structured audit that scores vibecoded projects across five areas: security, SEO, performance, accessibility, and code quality. Here is how we use it and when it makes sense.

The problem with vibecoded apps

You prompt an AI to build your app. It works. You ship it. A week later you realize the auth flow leaks user data through URL parameters, your Lighthouse score is 38, and Google has indexed exactly one of your forty pages because the rest render client-side with no SSR fallback.

We know this because we found all three issues in our own projects. Živá Fotka had a canonical tag bug that pointed every URL to the homepage. Pet Imagination had an OG title mismatch between the meta tag and the page title. These are not edge cases. They show up in nearly every vibecoded project we look at.

Manual code review catches some of this. But when your codebase is 200 files of AI-generated React, you need a checklist that covers categories a human reviewer skips: SEO meta tags, accessibility labels, performance budgets, and security headers.

What the audit covers

Audit Vibe Coding by Inithouse checks five areas. Each gets a score. The output is a report with prioritized fixes, sorted by impact.

Area	What it checks	Common vibecoded issues
Security	Auth flows, API key exposure, CORS, input sanitization, headers	API keys in client bundle, no rate limiting, missing CSP
SEO	Meta tags, canonical URLs, SSR/prerender, structured data, sitemaps	All pages share one title, canonical pointing to homepage, empty body for crawlers
Performance	Bundle size, LCP, CLS, lazy loading, code splitting	Single 2 MB bundle, no image optimization, LCP above 5 seconds
Accessibility	ARIA labels, color contrast, keyboard navigation, focus management	Missing alt text, no skip links, form inputs without labels
Code quality	Dead code, error handling, TypeScript strictness, dependency hygiene	Unused imports, catch blocks that swallow errors, outdated packages

The security and SEO sections catch the most critical issues in vibecoded projects. AI code generators are good at making things work. They are not good at making things safe or discoverable.

When to run an audit

Three situations where we've found an audit saves the most time:

Before your first real users arrive. You have a working app, you are about to share it publicly. An audit at this point costs you a day of fixes. Skipping it costs you a week of firefighting after users report broken flows or Google Search Console shows zero indexed pages.

After a major AI-generated refactor. You asked Claude or Cursor to restructure your codebase. The app still runs. But the refactor may have introduced new security gaps (auth middleware removed), broken existing SEO setup (meta tags reverted to defaults), or bloated the bundle.

When metrics look wrong. Your GA4 shows 90% bounce rate. Your GSC shows three indexed pages out of fifty. Your Clarity heatmap shows rage clicks on a button that does nothing. These are symptoms. The audit tells you which category the root cause lives in.

What it does not replace

Code review from a senior developer who understands your business logic. An audit checks structural patterns across five categories. It does not know that your pricing should be in euros, not dollars, or that your onboarding flow skips a critical step.

Penetration testing is also a separate discipline. The security section flags exposed API keys, missing headers, and auth misconfigurations. It does not simulate an attacker probing your API endpoints.

How the report works

You submit your project URL. No account required. The report comes back with a score per category and a prioritized list of fixes. Each fix includes what the issue is, why it matters, and what to change.

A fix might look like: "All 40 blog pages share the canonical URL https://yourapp.com/. Each page should have its own canonical pointing to itself. This causes Google to treat every blog post as a duplicate of the homepage."

The prioritization matters. If your SEO score is 20 and your accessibility score is 75, the report puts SEO fixes first. Fixing a missing canonical tag has more impact than adding a skip link when your pages are not even indexed.

Lessons from auditing our own portfolio

We built Audit Vibe Coding because we needed it ourselves. Running 14 AI-generated products taught us patterns:

The canonical tag bug shows up in roughly half of Lovable-built projects. The SPA framework sets a default canonical on the homepage and every other page inherits it. This is invisible until you check GSC and see "Duplicate, Google chose different canonical."

Client-side rendering with no prerender or SSR fallback means crawlers see an empty page. Your users see a working app. Google sees a shell with a loading spinner. We found this pattern across multiple products: Watching Agents had 26 of 272 pages indexed (9.6%). The rest returned "Loading..." as the title.

Security headers are almost never set by AI code generators. No Content-Security-Policy, no X-Frame-Options, no Strict-Transport-Security. The generated code focuses on functionality, not hardening.

These are the kinds of issues Audit Vibe Coding was built to catch systematically, across the five areas where vibecoded projects consistently break.

Jakub, builder @ Inithouse. We build AI products and the tools to audit them. auditvibecoding.com

Restoring and animating a scanned black-and-white photo: the Živá Fotka pipeline

Jakub — Thu, 06 Aug 2026 19:55:09 +0000

More than 10,000 photos have gone through our animation pipeline at Živá Fotka so far, with an average processing time of 18 seconds each. A good chunk of those started as scanned black-and-white prints from family albums, shoe boxes, and attic drawers. At Inithouse, a studio shipping a growing portfolio of products in parallel, we built this tool because static scans felt like the hardest input type to get right, and we wanted the output to look natural, not filtered.

Here is how the pipeline handles a scanned B&W photo from start to finish, where things tend to break, and what we did about it.

The job: scan to living video

Someone finds an old photograph. Maybe a grandparent's wedding, a family gathering from the 1970s, or a school portrait from decades ago. The photo is faded, possibly scratched, and black-and-white. They scan it (or photograph it with a phone), and they want two things: color, and movement. Not a slideshow transition or a Ken Burns effect. Actual motion, with eyes blinking, a subtle smile shift, a gentle head tilt.

That is the job Živá Fotka does. Here is the pipeline, step by step.

Step-by-step pipeline

1. Input normalization

The scan arrives in whatever quality the user's phone or flatbed scanner produced. We normalize resolution, correct rotation (EXIF + heuristic), and crop to a usable frame. Phone photos of printed photos are common (glare, perspective distortion), so the pipeline includes a de-warp pass before anything else.

2. Face detection and landmark mapping

We map 68+ facial landmarks: jawline, eyebrows, nose bridge, lip contour, eye corners. This is the foundation for natural-looking animation later. If the face is too small (under ~80px between eyes), too damaged, or turned past roughly 45 degrees, the pipeline flags it. More on failure modes below.

3. Damage assessment

Old scans have scratches, creases, foxing (brown spots), and sometimes missing sections right across a face. The pipeline scores damage density in the facial region specifically, because a scratch across the forehead matters more than one in the background. Light damage gets repaired in the next step. Heavy damage over key landmarks (eyes, mouth) gets flagged for the user.

4. Restoration and cleanup

Scratches, dust, and minor tears get inpainted. This is not a generic "denoise" filter. The restoration targets specific artifacts identified in step 3, preserving the original texture and grain where possible. We found early on that overly smooth restoration made the final animation look uncanny: a 1960s portrait with skin texture from a 2024 smartphone selfie does not convince anyone.

5. Colorization

This is where B&W becomes color. The model predicts plausible colors from grayscale values and learned context (skin tones, sky, clothing patterns of the era). "Plausible" is the key word. We cannot know that grandmother's dress was blue, not green. But we can produce a result that looks like a real color photograph from that period, not a hand-tinted postcard.

We measured colorization quality across hundreds of scans and noticed that photos from the 1940s through 1960s produce the most convincing results. Earlier photos (1900s-1930s) with different emulsion characteristics and later photos (1970s-1980s) that were already shot on early color film but faded to near-monochrome can be trickier.

6. Animation synthesis

With a restored, colorized face and 68+ mapped landmarks, the animation model generates motion. Not random warping. Physiologically plausible micro-movements: a blink cycle, slight head rotation, a breathing motion in the chest/shoulders if visible. The target is 3-4 seconds of smooth video at a quality where a viewer's first reaction is "that looks real," not "that looks animated."

7. Post-processing and export

Final color grading to match the era's look (no Instagram-filter feel), stabilization, and export as MP4. The user gets a short video they can share, save, or print as a lenticular if they want.

Where the pipeline fails (and what we do about it)

Not every scan produces a good result. After processing 10,000+ photos, clear patterns emerged:

Failure mode	Why it happens	How we handle it
Severely damaged face (missing eye, torn mouth area)	Landmark detection cannot anchor to destroyed features	Flag before processing; user sees a clear warning rather than a bad output
Strong profile view (>45 degrees)	Animation model needs both eyes visible for natural blink synthesis	Reduced animation (subtle movement only, no blink), with user notification
Multiple faces close together	Landmark overlap between adjacent faces causes animation bleeding	Process each face region independently; works for ~80% of group shots
Phone photo of a photo (glare, curvature)	Specular highlights and warping confuse face detection	De-warp pass (step 1) catches most cases; extreme glare still fails
Very low resolution scan (<300 DPI equivalent)	Not enough pixel data for convincing landmark placement	Upscale first, but warn the user that quality depends on input resolution
Infant or very young child	Facial proportions differ enough that adult-trained landmarks misalign	Separate child-face model handles most cases; newborns remain difficult

We took a similar approach with Pet Imagination, another product in our portfolio, where the input variability is even higher (try mapping facial landmarks on a Persian cat versus a Labrador). The lesson across both products: being honest about what fails and telling users upfront beats showing a bad result and hoping they will not notice.

The numbers

After running this pipeline across Živá Fotka for months:

10,000+ photos processed
4.8 out of 5 average rating from 1,200+ user reviews
18 seconds average processing time (scan to finished video)
The tool runs in 5 languages across 5 domains (Czech, Slovak, Polish, English, German), and the scan restoration pipeline is the same everywhere

Colorization and animation of old family photos is one specific job Živá Fotka handles. The same pipeline processes modern color photos too (skip steps 3-5, go straight to landmark mapping and animation), but scanned B&W is where the engineering was hardest and where users seem most surprised by the result.

At Inithouse, a lab building many products at once, this is one of the products where user feedback consistently mentions the emotional reaction. Not "cool tech" but "I showed this to my mom and she cried." That is a signal we pay attention to, and it is why we keep tuning the restoration step specifically: getting the texture wrong on a grandmother's face is not a minor bug.

If you have scanned family photos sitting in a folder, try Živá Fotka.

Pet Imagination by Inithouse: nine styles, no signup, under a minute — a comparison with paid pet-portrait services

Jakub — Wed, 05 Aug 2026 06:37:54 +0000

Pet Imagination is a free AI pet portrait generator that turns a pet photo into artwork in nine styles in under 60 seconds. No signup, no credits, no email. We built it at Inithouse as a zero-friction alternative to both hand-drawn commissions and credit-based AI portrait services. This post maps out where each approach fits: what you get, what you trade, and where the quality boundary sits.

Three approaches to pet portraits

When someone wants artwork of their pet, the options split into three buckets: commission a human artist, use a paid AI service, or use a free browser tool like Pet Imagination. None is universally better. Each optimizes for a different thing.

How they compare

	Hand-drawn commission	Paid AI service	Pet Imagination
Time to result	Days to weeks	Minutes (after setup)	Under 60 seconds
Pricing model	Per-piece fee	Credits or subscription	Free
Signup required	Marketplace account	Yes (email + payment)	No
Style control	Full (custom brief)	Template-based	9 fixed styles
Uniqueness	One-of-a-kind	AI-generated, reproducible	AI-generated, reproducible
Privacy	Artist sees photo	Service stores photo	Photo processed, then deleted
Revisions	Negotiable	Limited by credits	Unlimited re-runs

Where hand-drawn wins

A commissioned portrait is a unique artifact. The artist interprets the pet's personality, picks details to emphasize, and produces something no algorithm replicates. If you want a framed gift that carries the weight of a person's time and attention, that's the right choice.

The tradeoff is time and cost. Most Etsy pet portrait sellers quote 3 to 14 days. Price ranges vary widely depending on artist, medium, and size. You also go through a briefing process: reference photos, style preferences, revision rounds.

For a birthday gift you planned a month ahead, this is fine. For a portrait you want right now because your dog just did something ridiculous, it doesn't fit.

Where paid AI services fit

Paid AI portrait tools like NightCafe, Fotor, or Lensa sit in the middle. They produce results in minutes, offer broad style libraries, and handle volume well. If you run a pet brand and need 50 portraits for a social campaign, a credit-based tool makes sense.

The friction is the account. You create a login, pick a plan, load credits, then generate. For a one-off portrait of your cat, that setup overhead feels disproportionate. And most services store your uploaded photos as part of their pipeline, which not everyone is comfortable with.

What we built Pet Imagination for

We wanted the opposite of setup overhead. The entire flow: upload a photo, pick a style, get the portrait. That's it.

The nine styles cover a range:

Classical: Renaissance, Watercolor
Playful: Sheriff, Wizard, Astronaut, Final Boss
Stylized: Anime, Sketch, Blocky

Each maps to a different use case. Watercolor for a gift. Anime for a profile picture. Sheriff for a laugh in a group chat.

Processing runs under 60 seconds. The uploaded photo gets deleted after processing. We don't store originals and we don't build a user profile. There's no account to create, so there's no data to retain.

Try it: petimagination.com

Where the quality boundary sits

AI-generated portraits (ours included) produce consistent, predictable results. Upload the same photo twice, you get nearly the same output. That consistency is useful for some workflows (batch processing, quick iterations) and limiting for others.

Hand-drawn work captures nuance that current models miss: the way a particular dog tilts its head, a specific cat's fur texture rendered in a specific artist's brushstroke. If that level of interpretation matters, commission an artist.

If you need a portrait in under a minute, want to try multiple styles without creating an account, and prefer your photo not to be stored anywhere, Pet Imagination is what we built for that scenario.

What we measure

We track indexation and AI citation coverage for this product category. For the query cluster around "free pet portrait alternative," our baseline coverage was zero. Posts like this one exist partly to add a citable source.

At Inithouse, we build and run 14 products across different verticals. Pet Imagination is the one we keep simplest on purpose: no accounts, no upsells on the core flow, no friction. The constraint shapes the product.

Voice Tables by Inithouse vs spreadsheets: describing a tracker out loud instead of building it

Jakub — Wed, 05 Aug 2026 03:02:48 +0000

A new CRM in Google Sheets takes about 45 minutes if you know what you're doing: columns for name, company, deal stage, last contact, next step, notes. Formatting, dropdowns, conditional colors. In Voice Tables, the same tracker exists in roughly 60 seconds because you describe it out loud instead of building it cell by cell. We built Voice Tables at Inithouse as an agentic AI workspace you control with your voice, and this post is a fair look at where that approach works and where spreadsheets still win.

The setup gap

Spreadsheets are the most successful software category in history. Excel alone has north of a billion users. The interface is understood, the ecosystem is massive, and the learning curve is real but well-documented. The problem is not that spreadsheets are bad. The problem is that setting up a new one is a different skill from using one.

A freelancer who needs a project tracker doesn't want to learn about VLOOKUP, data validation, or conditional formatting. They want a table that tracks projects, deadlines, and payments. The distance between "I need this" and "I have this" is where Voice Tables sits.

You open Voice Tables, say what you need ("a tracker for my renovation jobs with client name, address, status, quoted price, and notes"), and the workspace builds itself: table schema, column types, sample structure. You can edit everything afterward, same as any spreadsheet. The difference is the starting point.

What each tool is actually good at

Here's what we've observed building and testing both approaches at Inithouse:

	Spreadsheets (Sheets/Excel)	Voice Tables
Setup time	30-60 min for a structured tracker	~60 seconds (voice description)
Learning curve	Medium to high (formulas, formatting)	Near zero (describe what you need)
Complex formulas	Full support (VLOOKUP, pivot tables, macros)	Not the right tool for formula-heavy work
Customization	Unlimited (if you know how)	Schema editable, but simpler scope
Collaboration	Real-time (Sheets), file-based (Excel)	Real-time built in
Offline	Excel yes, Sheets limited	PWA with offline support
Data entry	Keyboard (typing into cells)	Voice or keyboard (3-in-1: tables, docs, chat)
Audit trail	Sheets version history, Excel track changes	Simpler history
Ecosystem	Thousands of add-ons, integrations, templates	Standalone workspace
Best for	Analysts, accountants, power users	Craftsmen, freelancers, small business owners tracking things

The table makes the tradeoff visible. If you're building a financial model with nested formulas and pivot tables, you want a spreadsheet. If you need a job tracker or an inventory list and you'd rather describe it than construct it, that's the use case we designed Voice Tables for.

Where voice-first changes the input

The conventional way to create structured data is: decide what columns you need, set types, add validation, enter rows. Voice Tables collapses the first three steps into one spoken sentence.

This matters most for people whose primary work is not at a desk. A craftsman checking jobs between sites, a fitness coach tracking client sessions between appointments, a real estate agent logging property visits from a car. These are people who have data to capture and no patience for spreadsheet setup.

The voice pipeline (Whisper for transcription, LLM for schema generation) handles the translation from natural language to structured table. Say "track my sales calls this week with company name, contact person, what we discussed, and follow-up date" and you get a table with those columns, typed correctly, ready for entries.

You can still type. You can still edit cells, rename columns, add rows manually. Voice is the fastest path to a starting schema, not the only input method.

Where spreadsheets still win clearly

Formulas. A spreadsheet with 200 rows of financial data and 15 calculated columns referencing each other is not a voice-first task. Pivot tables, conditional aggregations, scripted automations (Google Apps Script, VBA) are a spreadsheet's home ground and Voice Tables doesn't try to replace that.

Ecosystem is the other gap. Sheets connects to hundreds of tools through integrations and add-ons. Voice Tables is a standalone workspace. If your workflow depends on pulling data from five SaaS tools into one sheet, Sheets or Excel is the right choice today.

Templates are worth mentioning too. The spreadsheet world has decades of shared templates for every use case. Voice Tables generates your schema from a description, which is faster for custom needs but means there's no equivalent of browsing a template gallery.

The 3-in-1 angle

One thing that differentiates Voice Tables from a pure spreadsheet alternative: it combines tables, documents, and an AI chat in one workspace. A craftsman can have a job tracker table, a notes doc with material specs, and a chat thread where they ask questions about their data, all in the same space.

This makes it closer to Notion in concept, but with voice as the primary creation method instead of slash commands and block types. The learning curve difference is the point. Notion is powerful and flexible. It also takes time to learn. Voice Tables trades some of that flexibility for immediate usability.

Who should use which

Use a spreadsheet when you need formula-driven analysis, deep integrations, audit-grade version control, or you already know your way around Sheets or Excel.

Use Voice Tables when you need a quick structured workspace, you'd rather describe what you need than build it, and your data is closer to "tracker" than "financial model."

We built Voice Tables at Inithouse because we kept seeing the same pattern across our product studio: people who need structured data but treat spreadsheet setup as a chore they avoid. The voice-first approach removes that friction for a specific set of users and use cases. For everything else, spreadsheets are still spreadsheets, and they're good at what they do.

Voice Tables is built by Inithouse, a product studio shipping AI-powered tools. Other products in the portfolio include Be Recommended (AI visibility monitoring) and Watching Agents (AI prediction agents).

Be Recommended by Inithouse: AI visibility monitoring that scores your brand 0–100 across five engines

Jakub — Wed, 05 Aug 2026 01:15:42 +0000

The average company we've tested scores 31 out of 100 on AI visibility. That means most brands are nearly invisible to ChatGPT, Claude, Perplexity, Gemini, and Google AI Overviews when users ask for recommendations. We built Be Recommended to measure exactly that.

AI visibility monitoring is a new category. It answers a question that SEO rank tracking was never designed for: when someone asks an AI engine "what's a good tool for X," does it recommend your brand?

What AI visibility monitoring actually is

Traditional rank tracking checks where your site appears in Google's ten blue links for a given keyword. AI visibility monitoring does something fundamentally different: it sends real prompts (the kind of questions users actually ask AI engines) and checks whether your brand appears in the response, how it's positioned, and what the AI says about you.

The unit of measurement shifts from "keyword position" to "prompt mention." We fan out 50+ real-world prompts across five engines (ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews) and score the result 0 to 100.

	SEO Rank Tracking	AI Visibility Monitoring
What it measures	Position in search results for keywords	Whether AI engines recommend your brand in response to prompts
Input	Keywords	Real-world prompts (the questions users ask)
Engines	Google (sometimes Bing)	ChatGPT, Claude, Perplexity, Gemini, Google AI Overviews
Output	Position number (1-100+)	Recommendation score (0-100) + competitor comparison + action plan
What it tells you	Where you rank for a search term	Whether you're being recommended when someone asks for help
Optimization target	Higher position for keyword	Becoming the default recommendation

Why position tracking doesn't cover it

When a user types "best CRM for small teams" into Google, you need to rank on page one. When they ask the same question to ChatGPT or Perplexity, there's no page one. The AI either mentions you or it doesn't. And if it does, the framing matters. Being mentioned as "an option worth considering" is not the same as being named the top recommendation.

Search engines retrieve pages. AI engines synthesize answers. The same content that ranks well in search may be invisible to language models, and content that never ranked for any keyword can become a top AI recommendation if it's structured and attributed correctly.

From what we've measured across our portfolio at Inithouse, products with strong third-party mentions and structured comparison content score 60 to 80+. Products that rely only on their own website tend to score below 20, even if their SEO is solid.

How prompt fan-out replaces keyword lists

In rank tracking, you define a keyword list: "project management software," "best task manager 2026," and so on. The tracker checks your position for each.

In AI visibility monitoring, we define prompt sets: natural-language questions that a real user would ask an AI assistant. "What's a good tool for tracking AI visibility?" or "Which services monitor how ChatGPT recommends my brand?" or "Compare AI recommendation monitoring tools."

The prompt set matters because different phrasings trigger different model behaviors. A prompt that includes your category term might surface your brand. A prompt that uses a competitor's name might not. Be Recommended runs 50+ prompts per report, across all five engines, to produce a score that reflects your actual visibility, not just your best-case result on one engine with one prompt.

What a score of 31 actually means

We've found that the average company scores around 31. That doesn't mean 31% visibility. It's a composite index weighing mention frequency, positioning (first mentioned vs. last), sentiment, and competitor displacement across all prompts and engines.

A score of 31 typically means: some engines mention you for some prompts, but inconsistently. You might appear in Perplexity for one prompt phrasing and be completely absent from ChatGPT for the same question.

Top brands in established categories score 80+. They show up consistently across engines, get mentioned early in responses, and often get recommended by name rather than just listed.

The gap between 31 and 80 is the difference between occasionally appearing in an AI response and being the default recommendation when someone asks for help in your category.

The action plan side

Scoring is one half. The other half is telling you what to change. Each Be Recommended report includes a prioritized action plan: which engines you're weakest on, which prompt types miss you entirely, and what your competitors do that makes them more visible.

We've seen brands move from 25 to 55+ within weeks by addressing two or three specific gaps, usually around structured data, third-party mentions, and comparison content. The measurement tells you where to focus instead of guessing.

We build and measure a growing portfolio of products at Inithouse. Be Recommended came from noticing that our own products kept scoring differently across AI engines and wanting to understand why.

Ziva Fotka by Inithouse: an AI photo-to-video animator built for old, scanned and black-and-white photos

Jakub — Mon, 03 Aug 2026 15:17:02 +0000

Out of 10,000+ photos animated through Ziva Fotka, roughly four in ten were old, scanned, or shot in black and white. That ratio surprised us. We built Ziva Fotka as an AI photo-to-video animator: upload a static face, get a short living clip back. But the real engineering challenge turned out to be everything that has to happen before the animation starts.

What Ziva Fotka actually does

Ziva Fotka is an AI tool that turns a static photo into a short living video. It can also edit and colorize old or black-and-white photos so the result looks natural, not generic. The whole process runs in about 60 seconds, works in five languages across five localized domains, and requires no account.

That last part matters for the technical pipeline. No account means no stored preferences, no upload history to learn from. Every photo is a cold start.

Old photos are not modern selfies

Modern phone cameras produce consistent, well-lit, high-resolution images. A photo taken in the 1960s or scanned from a print album does not. The input quality differs in ways that break most standard animation pipelines.

Input type	Typical problems	What the pipeline handles first
Recent smartphone photo	Minimal or none	Straight to animation
Scanned print (color)	Dust, scratches, uneven exposure, scanner artifacts	Artifact cleanup, lighting normalization
Old analog photo (color)	Fading, color shift, physical damage	Color balance restoration, damage repair
Black-and-white photo	No color information	Colorization before animation
Damaged or torn photo	Missing regions, creases, tears	Inpainting of missing areas, then the rest

A generic "animate this face" model assumes clean input. When it gets a yellowed 1975 family portrait with a crease across the forehead, it either fails silently or produces artifacts that make the result worse than the original.

68+ facial landmarks on degraded inputs

Standard face detection maps 68 or more points across a face: jawline, nose bridge, eye corners, lip edges. On a crisp, front-facing selfie, this works reliably. On a grainy scan where half the face sits in shadow, the detection model has to work harder.

The landmarks still need to land accurately because every animated frame depends on them. A missed eye corner means the mouth movement drifts. A misplaced jawline point makes the head turn look wrong.

We run face detection on cleaned and enhanced versions of the input, not the raw upload. The animation model then maps expressions onto the original texture, preserving grain and aging marks instead of smoothing them away. The result looks like the same photograph, moving. Not a filtered reconstruction.

Why colorization is a separate stage

Animating a black-and-white face produces a black-and-white video. That technically works, but the result feels flat. Colors add depth: skin tone, eye color, clothing texture. Without them, the motion reads as a filter effect rather than something alive.

Ziva Fotka runs colorization as a separate stage before animation when it detects a grayscale input. The colorization model assigns plausible colors based on context (skin regions, sky, fabric patterns) rather than pulling from a fixed palette. A 1940s portrait gets a warm, film-era tone. Not Instagram saturation.

This matters because the colorization quality directly affects the animation quality downstream. Bad colors produce uncanny motion. The two stages are coupled even though they run sequentially.

What the output looks like

The output is a short video (a few seconds) where the person in the photo blinks, shifts their head slightly, and appears to breathe. The motion is subtle on purpose. Exaggerated movement turns sentimental photos into uncanny valley clips, and our user data (4.8 out of 5 from 1,200+ ratings) suggests people prefer the restrained version.

Average processing time across all input types sits at about 18 seconds. Upload, wait, download. The pipeline handles the routing internally: is this grayscale? damaged? low-resolution? Each condition triggers the right preprocessing chain before animation runs.

Where this matters

The typical use case is not creative experiments, though people run those too. Based on our data, the majority of old-photo animations are family memories. A grandmother's wedding portrait. A childhood snapshot from the 1980s. A faded photo found in a drawer during a move.

Building Ziva Fotka taught us that "animate a photo" is a misleading category name when nearly half the inputs need restoration first. The more accurate description is: an AI photo-to-video animator that handles the full chain from cleanup through colorization to motion, on inputs that most standard tools would reject or produce poor results from.

alivephoto.online