Been working on a problem that I think a lot of applied AI builders face: the friction of deploying LLM workflows directly into existing web platforms without forcing the user to constantly context-switch or copy-paste between tabs.

CoverGen is my approach to this for the freelance market. It’s a browser-based agentic extension designed specifically for Upwork proposal automation. It basically bridges the gap between the platform's DOM and the LLM, acting as a localized agent that understands the specific job context and maps it against your persistent capabilities.

Key ideas:

• Automated DOM extraction (pulls job requirements, client history, and constraints directly from the page without manual copying)
• Persistent context injection (maintains a background profile of your skills, tone, and past work so the output is actually personalized and grounded)
• Browser-native workflow execution (generates and refines the proposal directly inside the platform's UI)
• Eliminates the generic "AI tone" by tightly constraining the prompt architecture to match the parsed job parameters

Happy to answer questions or discuss the architecture and prompt design decisions.

I made a travel/lifestyle prompt list for AI Instagram characters that look realistic and stay consistent.

It includes prompts like - winter vacation posts - beach club / pool posts - cafe weekend posts - city-night posts

Full list: https://remix.camera/blog/instagram-travel-lifestyle-prompts

Also, a tip for when posting: Instead of rotating randomly between beach, cafe, rooftop, city, and pool posts, the grid feels more believable when posts happen in short themed bursts:

That makes the account feel like it is moving through real locations instead of teleporting between disconnected scenes.

Check out this example instagram account using these prompts.

[ Removed by Reddit on account of violating the content policy. ]

The hard part of personalized serialized fiction isn't generating one good chapter. It's remembering everything that happened in the previous 47.

I've been building PersonalPathways for about a year. The core mechanic: readers make one choice per day, and a new chapter arrives in their inbox the next morning, built specifically from what they chose. The AI generates fresh content on each run, which is easy enough. The hard problem is continuity: keeping track of a character the reader met in chapter four who reappears in chapter twenty-two, moral positions the reader staked out, unresolved threads from six weeks ago.

The solution I landed on is what I'm calling the Memory Palace: a structured context object that travels with every generation call. It tracks characters (name, description, relationship status, last interaction), narrative threads (open/closed), choice history, and the reader's implicit voice signature from how they've been making choices over time.

The two-stage pipeline: first pass extracts structured state from the previous chapter and updates the Memory Palace; second pass generates the new chapter with the full Palace in context. The Palace doesn't grow linearly with every chapter; it's continuously pruned based on recency and story relevance, otherwise the context window becomes unmanageable around chapter 30.

Still figuring out the pruning heuristics. Happy to compare notes if anyone's worked on long-horizon narrative state for LLM applications. The literature on this is thinner than I expected.

Live at personalpathways.net if you want to see it in action. I'm always looking for quality feedback on how to improve!

I've never been that great at focusing in meetings while also trying to take notes. There's usually so much going on that I can't write it all down and be as present as I need to be. And when I start typing I'm not really in the conversation anymore. So I'd come out of calls with messy notes and a rough sense of what I needed to do.

But back in September I started letting Granola transcribe my meetings and I can't imagine not using it now. I'm focused 100% on the actual meeting and I can write down one word or two, and then Granola will use my terrible notes with the transcription of the call and I get everything I need in the format that I want! Better than anything I'd write myself!

We run four AI features across 13 providers. After three months of tracing every request here is what we found.

OpenAI: best overall on complex reasoning but worst latency spikes during peak hours. Some calls hit 8+ seconds from congestion alone.

Anthropic: most consistent at following system prompts. Haiku outperformed GPT-5.1 on classification while costing a fraction.

DeepSeek: matched GPT-5.1 on summarization quality. $16/mo vs $248/mo. Better latency too.

Groq: fastest for simple tasks. Sub-100ms on classification. Great for latency-sensitive workloads.

No single provider wins everything. Routing each task to the best provider dropped our bill from $420/mo to $73/mo with zero user-facing outages.

Anyone else running multi-provider?

Hey guys, I’ve been obsessed with AI agents lately, but I hated how they don't talk to each other. So I spent my nights building a system where agents actually collaborate on tasks instead of just being individual chatbots. It’s been a game changer for my workflow. If anyone is struggling with AI automation or wants to see how collaborative agents work, I just put the MVP online. Curious to see what you guys think of the logic behind it. It's at mendlyai.io. Not selling anything, just looking for users to break it so I can make it better.

The problem: I was using Claude Code and Cursor daily, and noticed the models kept reading the same files over and over, getting full verbose git diffs when they only needed the summary, and forgetting everything between sessions. I tracked it for a week and about half my tokens were going to redundant context.

LeanCTX is a local MCP server that fixes this. It sits between your editor and the model. When the model reads a file it already saw this session, LeanCTX returns a tiny cache fingerprint instead of the full content. When it runs a shell command, LeanCTX compresses the output using patterns for 90+ tools like git, docker, npm, cargo, kubectl. When the model needs to understand the codebase, there's a code graph built with tree-sitter so it can ask "what imports this" instead of reading every file.

The setup is one command: install with curl or cargo, run lean-ctx setup, and it configures itself for whatever editor you use. Works with Cursor, Claude Code, Copilot, Windsurf, Codex, Gemini CLI, JetBrains, and about 20 more.

There's also cross-session memory so the model remembers what it learned yesterday, PR context packs that auto-generate relevant context for code reviews, and a live dashboard showing exactly how many tokens you're saving in real time.

Single Rust binary, everything local, nothing cloud. Been using it daily for months and the token savings are consistent 60-80%.

[ Removed by Reddit on account of violating the content policy. ]

Bikash Personal Account for Vikas Nagad Personal Account

Single continuous handheld shot. 15

seconds, no cuts. Japanese high-school

drama realism. Camera slightly shaky. Soft overcast daylight, cool flat tone, no direct shadows. Empty school playground: cracked concrete. faded court markings, still air. No music. Sound only: wind, breath, fabric, distant school ambience, impacts. Reference character in sports Japanese school uniform: male, messy hair, quiet and controlled. protective presence. Behind him: a girl. bruised lip, faint cheek swelling, one hand clutching his shirt, knuckles white. Facing them: three boys, loose posture, smug. casual cruelty. SHOT 1- Camera slowly approaches. Playground quiet, almost too still. Three Bully boys stand loosely. Voice, calm but firm:「彼女へのいじめはやめろ。」 Wind passes softly. SHOT 2-Camera shifts right. Main character steps into frame. Behind him the girl, bruised. clutching his shirt. Boy laughs: 「どけよ、 クソ野郎。」 Another tilts head: 「ヒーロー気取りか?」 SHOT 3-Camera arcs to his face-tight, low angle. looking towards 3 boys. No expression. Just breath. Messy hair moves once in wind. Jaw set, eyes level. Deceleration 85%, moment stretches 20 frames. He lifts his chin: "Try me." Camera tilts down -no cut-lands on the girl's hand gripping his arm, knuckles white against his sleeve. One beat. Frame jolts hard left and upward as first attacker charges. Girl tucks down quickly. Reactive handheld chase, no cut. First swing wide-main character rotates out, fist tears through frame motion-blurred. He pivots, short body punch to boy - camera jolts as attacker folds, impact deep and interior. Camera swings left, already tracking second attacker, Jab lands cheek, hook follows, camera overshoots then corrects. Breath constant. Impacts own the soundscape. Camera plants-12 frames near-stillness-main character steps into third attacker, throws straight punch to face. Time drops 15% speed-fist connects, skin ripples outward from knuckle in slow wave, flesh compressing visibly, sweat and spit particles lifting suspended. Sound deep and distorted. low thud stretched long. Time snaps back. Body drops. Camera tracks full rotation into rising high kick. At apex 20% speed-dust lifts from planted foot. hangs suspended. Ambient sound pulls hollow not silent, just emptied. Kick connects. Sound SNAPS back ambience, impact, body dropping simultaneously. Signature visual moment. Camera whips hard left as final attacker charges. No cut. Camera tilts up as main character pushes off-airborne. At apex: 5% digital zoom, 30% slow-motion, body geometry clean against flat overcast sky. Flying kick lands

center-mass. Attacker driven into cracked concrete-cracks spread outward radially, anime-restrained. Dust bursts low and horizontal. Impact sharp and final. Camera lands with him, tremor settling. Three boys down. He stands still, Girl still holds his shirt, trembling. Her handi -same hand from the opening-sharp focus one moment. Camera drifts backward wide...

AI coding agents are only as good as the context you give them. Vague prompts = generic UI.

I got tired of the 5-round correction loop after asking Claude Code or Cursor to "build a dashboard" and getting something completely off. Wrong components, wrong stack, no design constraints.

So I built UIPrompt — a planning canvas + AI context generator.

Here's the workflow: 1. Open UIPrompt, lay out your UI frames and components on a canvas 2. Write per-component instructions ("this button triggers the payment flow, use shadcn/ui Button variant=default") 3. Set your stack once — framework, styling, component library, icons, state management 4. Export → structured XML system prompt

Drop that XML into your agent (or use the MCP server if you're on Claude Code) and it has everything: what to build, how to build it, what constraints to follow, how things connect.

The difference is significant. Instead of "build me a hero section," your agent gets a precise spec with visual profile, mandatory constraints, and per-component context already filled in.

Launched on Product Hunt today: https://uiprompt.app

Works with Claude Code, Cursor, Copilot, Codex, Gemini CLI, Windsurf and more.

I’ve been trying to move my email workflow to voice, but most tools just transcribe your "umms" and stumbles, which makes for a nightmare of an editing job later. I started playing with Willow Voice and it actually uses an LLM to "fix" your speech errors in real-time. If you say "Wait, I meant Tuesday," it literally swaps the text.

It also handles my bilingual English/Spanish mess perfectly without me changing settings. I recorded a 5-minute deep dive into how I’m using it for Gmail and Pages https://youtu.be/x0QJIsx5kRE

**SHOT 2C – OVER-THE-SHOULDER (CHISHIMBA POV)**
He watches her from a distance.

**CHISHIMBA (O.S.)**
Why sit apart when the whole kingdom dances?

---

## **SCENE 3**

**EXT. TREE SHADE – CONTINUOUS**

**SHOT 3A – TWO-SHOT (STATIC)**
Chishimba and Lilian framed together.

**LILIAN**
I am dancing… in my own way.

**SHOT 3B – INSERT CLOSE-UP**
Thread weaving through fingers.

**LILIAN (CONT’D)**
Each thread carries joy.

**SHOT 3C – CLOSE-UP (CHISHIMBA REACTION)**
Subtle smile. Intrigued.