Platforms

One canonical draft.
Platform-native outputs.

FromSource keeps source, project context, and style intact, then reshapes the same core idea for 11 platforms — from X to Xiaohongshu, YouTube to Medium — without turning the output into copy-paste filler.

X
Sharp take. Minimal waste.

On X, the job is compression. FromSource turns the canonical draft into short, direct posts or thread skeletons that preserve the strongest claim and emotional edge without sounding like scheduled marketing copy.

Format Specification
Post format1–3 short paragraphs or thread skeleton (up to 10 tweets)
Character limit280 per tweet · Thread expands freely
Tone targetDirect, punchy, opinionated · No hedging
Hook styleContradiction, surprising stat, or strong claim in tweet 1
Hashtags0–1 · Only if highly specific
CTAOptional final tweet — retweet, reply prompt, or link
AK
Alex Kim
@alexkim · 2m
Most AI still treats humans as input machines.

That's the wrong model — and it's why most voice agents feel robotic even when the words are right.

The next unlock is emotional coherence. 3-year thread from the trenches →
Directness
Very high
Punchiness
High
Formality
Very low
Promotion
Minimal
LinkedIn
Structured insight. Human tone.

LinkedIn needs more structure and more warmth than X. FromSource reshapes the same canonical idea into a hook, clear supporting points, and a discussion-worthy takeaway while keeping it grounded in your actual work.

Format Specification
StructureHook line → 2–4 bullets → takeaway → question CTA
Length150–250 words optimal · Stays above fold at 3 lines
Hook stylePersonal story, surprising number, or contrarian statement
Tone targetProfessional but warm · Personal experience foregrounded
Hashtags3–5 at end · Niche over broad
CTAOpen question to drive comments
AK
Alex Kim
Founder · AI voice systems · 2nd
3 years building voice AI taught me the thing nobody talks about. The bottleneck isn't the model. It's the gap between what a user said and what they meant.

Here's what I've learned:
Accuracy ≠ resonance — the words can be right and still feel wrong
• Latency below 200ms isn't about speed — it's about perceived presence
• Emotional coherence is a systems problem, not an NLP problem

The teams getting this right are asking better questions, not using better models.

What's your experience building for human-AI alignment?
Directness
High
Warmth
High
Formality
Medium
Structure
Very high
Reddit
Context first. Always.

Reddit punishes promotion and rewards honest operator context. FromSource removes marketing tone, front-loads the situation and observation, and preserves the real question behind the post so the output reads like a builder asking peers, not a funnel in disguise.

Format Specification
StructureContext → observation → genuine question
Tone targetPeer-to-peer · No marketing language · First-person observation
Self-promoMinimal to zero · Attribution buried if needed
Length100–300 words · Longer for technical depth subs
CTAGenuine question · Invites contrary views
u/
u/alexkim_ai
r/MachineLearning · 3h · 847 upvotes
Context: I've been building voice agents for 3 years, most recently focused on emotional coherence in conversational systems.

Something I notice that doesn't get discussed: the bottleneck in most production voice AI isn't the model — it's the gap between what the user said and what they meant.

Curious if others are seeing this. What approaches have worked for you when literal input and intended emotional context diverge?
Authenticity
Critical
Self-promo
Minimal
Context
Essential
Questions
Always
Facebook Pages
Accessible. Clear. Shareable.

Facebook Pages needs the same idea translated for a broader, less insider-heavy audience. FromSource softens jargon, opens with a clearer setup, and keeps the post approachable without flattening the substance.

Format Specification
ToneFriendly, accessible, conversational · Story-forward
JargonMinimized · Explained when necessary
Length80–180 words · Punchy paragraphs
Media promptSuggests image/graphic where relevant
EngagementReaction prompt or share CTA
AK
Alex Kim
AI Founder · 4h · 🌐 Public
Here's something I learned after 3 years building AI products:

The technology isn't the hard part. Getting AI to actually understand what you mean — not just what you said — is the frontier nobody's cracked yet.

Imagine an AI assistant that picks up on your tone, not just your words. That's what the next generation of voice AI looks like. We're working on it every day.

What would you want an AI to understand about you beyond your words? Let me know in the comments 👇
YouTube
Anchor asset. Metadata bundle.

When video is the anchor asset, FromSource builds the surrounding metadata package as part of the same workflow: title variants, description, chapters, pinned comment, and linked downstream posts. It is bundle-aware, not an isolated text generator.

Format Specification
Title3 A/B/C variants · Curiosity-first · Under 60 chars
DescriptionFirst 2 lines hook (above fold) · Full summary · Natural keywords · Links section
ChaptersTimestamped · Descriptive (not generic "Intro")
Pinned commentResource links + timestamp highlights + engagement prompt
Tags15–20 tags · Mix of broad and niche
YT
YouTube Metadata Bundle
3 title options · description · chapters · pinned comment
Titles (A/B/C)
A: Why Emotional AI Changes Everything (Not What You Think)
B: I Built Voice AI for 3 Years — Here's What Nobody Talks About
C: The Hidden Bottleneck in AI Products (It's Not the Model)
Chapters
0:00
Intro — the wrong mental model
2:14
What emotional coherence actually means
7:40
3 patterns from production systems
14:22
Where the field goes next
Pinned Comment
Resources + key timestamps in this comment. Drop your take below — especially if you've built in this space. I read everything.
Also supported

Six more destinations,
same source-first flow.

Same canonical draft, same style profile, assisted publish everywhere. Additional platforms use the copy-and-open pattern until direct-post APIs are wired.

Threads
Conversational, chained thoughts

Short-form variant tuned for Threads' looser rhythm. Chain-friendly opener, no marketing polish, no hashtag pile-on.

Assisted · copy + open composer
Bluesky
Community-native, 300 chars

Compressed variant that fits Bluesky's 300-character ceiling and skips crossposting artifacts. Reads like a person, not a syndication feed.

Assisted · copy + open composer
Instagram
Caption + cover, mobile-first

Cover image + caption pair. FromSource generates the caption from the canonical draft and pairs it with the cover version you approved in Compose.

Assisted · copy caption + open studio
Xiaohongshu
Visual + note, discovery-first

Note-style variant with title, body, and tag cues shaped for the Xiaohongshu discovery model. Cover image pairs with the generated caption.

Assisted · copy + open composer
Substack
Long-form, subscriber voice

Long-form variant with a stronger opener, subhead structure, and closing question. Keeps source citations available for the body.

Assisted · copy + open editor
Medium
Essay-shaped, structured

Essay-shaped variant with headings, callouts, and a clear takeaway. Content-type aware so guides and release notes structure differently.

Assisted · copy + open editor
Eleven platforms. One system.

Write once at the core.
Adapt everywhere on purpose.

Try the full multi-platform flow free on the web, then drop into the App Store build when you want the native desktop and mobile surfaces.

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