DEV Community: Aishani

SlothDigest 🦥: Rewarding Laziness with Open-Weight AI

Aishani — Thu, 22 Oct 2026 09:05:05 +0000

Tomorrow is International Sloth Day, and our favorite DEV moderator Sloan the Sloth understands something fundamental: energy conservation is an art form.

Yet everyday, developers and humans burn mental stamina reading 50-message Slack threads, chaotic family group chats, or petty email chains, asking the same exhausting question: "Do I actually need to care about this?"

For the Slow and Steady challenge, I built something that honors Sloan by doing the work so you don't have to: SlothDigest.

🌐 Live Demo & Code

🚀 Try the Live App: https://slothdigest.streamlit.app
💻 GitHub Repository: https://github.com/aisha453/SlothDigest

🌟 What I Built

SlothDigest takes any messy dump of unread messages and gives you:

🟢 The Urgency Traffic Light: Tells you in plain English if you can safely go back to sleep (🟢 Green), skim it later (🟡 Yellow), or if you actually need to wake up and act (🔴 Red).
📋 The 2-Line Sleepy Summary: Strips away every bit of drama, emotional venting, and fluff to give you:
- Line 1: What happened (pure facts).
- Line 2: The bottom line / consensus.
💤 1-Click Zero-Brain Canned Replies: Three pre-written responses you can copy with one click:
- The Minimalist ("Sounds good! 👍")
- The Graceful Delayer ("Reviewing this later this afternoon, thanks!")
- The Diplomatic Delegator ("Thanks for handling this, happy to follow your lead!")
⚡ Energy Saved Meter: Calculates how much human time and mental stamina was spared.
☀️ / 🌙 Day & Night Themes: Toggle between high-contrast Day Mode and deep slate Night Mode with a single click.

🦥 Why Open Innovation Matters

The prompt asks: Tell us where your open-based approach worked better than a closed one. Here is why open-weight models are the beating heart of SlothDigest:

Absolute Privacy for Personal Messages: Group chats and internal work threads contain private jokes, confidential roadmap discussions, or family gossip. Sending this raw text into proprietary closed-API black boxes means risking your data being used to train future corporate models. With open-weight models, you retain full sovereignty.
True Local Portability with Ollama: Because SlothDigest is built on open standards, you can switch from Groq's cloud to a 100% offline, air-gapped Ollama model running locally on your laptop with zero code changes. It works on an airplane or cabin in the woods with zero internet connection.
Zero Cost Forever: By taking advantage of Groq's ultra-fast inference on open weights, anyone can clone this project, run it for $0, and never worry about unexpected credit card bills or API subscription paywalls.

🛠️ Tech Stack

AI Engine: Open-weight intelligence on Groq LPU cloud; fully compatible with local llama3.1 via Ollama.
Backend / Schema: Python 3.11 + Pydantic v2 for structured JSON enforcement.
Frontend: Streamlit with custom Day/Night styling and clipboard integration.

💤 Conclusion

You don't need a 200-page agentic orchestration framework to solve everyday burnout. Sometimes, all you need is a sleepy open-weight model that reads the drama and tells you: "Nothing needs your attention. Go back to sleep."

Happy International Sloth Day! 🦥

InkFlow: The Adaptive Writing Twin & Anti-AI-Slop Engine

Aishani — Sat, 17 Oct 2026 16:48:00 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle

What I Built

We are living through a time when AI-generated writing is becoming increasingly common, but much of it sounds remarkably similar.

When people use generic AI assistants to polish emails, academic papers, or technical reports, their original writing style can get replaced by repetitive buzzwords, predictable sentence structures, and a uniform corporate tone.

For students, researchers, professionals, and content creators, this creates a problem: their writing can lose the personal voice and natural rhythm that make it their own.

That's why I built InkFlow: The Adaptive Writing Twin & Anti-AI-Slop Engine.

InkFlow is an adaptive writing assistant designed to help people improve their writing while preserving their individual style.

It has four core capabilities:

Personal Style Genome: Uses a two-minute linguistic calibration exercise to build an eight-dimensional profile of a user's writing style, including formality, sentence-length variation, vocabulary diversity, clause structure, and punctuation habits.
AI-Slop Detection: Identifies common synthetic writing clichés and highlights phrases that make a draft sound generic.
Four-Stage Rewrite Pipeline: Generates or transforms text, removes unwanted stylistic patterns, checks semantic consistency, and performs a light grammar pass designed to preserve the author's punctuation preferences.
Adaptive Style Learning: Tracks writing changes across sessions and incorporates recurring stylistic preferences rather than overreacting to a single edit.

InkFlow is designed for university students, academics, non-native English professionals, content creators, and technical writers who want writing assistance without losing their own voice.

The Side Hustle: Monetization

InkFlow follows a freemium model.

Free Tier: Includes 1,500 words per day, one personal Style Genome profile, AI-slop highlighting, and access to the Humanize Studio.

Pro Tier — ₹39/month: Offers unlimited daily words, multiple writing profiles for different contexts, and priority access to higher-capacity open-weight models through an integrated Razorpay checkout supporting UPI, cards, and net banking.

The goal is to make personalized writing assistance affordable for students and independent creators.

Demo

Live Web Application: https://ink-flow-twin.vercel.app

Backend API: https://inkflow-backend-73hf.onrender.com

Demo Video:

hacktoberfest3rdchallenge.mp4 - Google Drive

drive.google.com

Here are some features you can explore:

Humanize Studio: Paste a draft or raw bullet points, identify common AI-writing clichés, and refine the text with character-level punctuation differences.
Two-Minute Voice Calibration: Complete four linguistic exercises to generate a personalized Style Genome.
Sentence Cadence Analytics: Inspect sentence-length variation across paragraphs.
Direct AI Writer: Draft emails and lecture notes, then refine them through the Humanize Studio.

Code

GitHub Repository: https://github.com/SoumyaGhosh2006/InkFlow

The project uses a modular full-stack architecture.

Backend: FastAPI, Python 3.11+, Motor for asynchronous MongoDB access, Groq inference, Pydantic v2, Razorpay integration, Sentry tracing, and Pytest.

Frontend: React 19, TypeScript, Vite, Tailwind CSS, Framer Motion, Zustand, Radix UI, Lucide icons, and Firebase Authentication.

The repository includes the implementation of the writing pipeline, style calibration, authentication, and subscription workflow.

How I Built It

1. Dual-Tier Open-Weight Inference

Rather than depending on a single model, InkFlow uses a primary model and a fallback model through Groq.

Primary model: openai/gpt-oss-120b handles complex stylistic transformations, nuanced rewriting, and Style Genome conditioning.
Fallback model: openai/gpt-oss-20b provides a lighter alternative when the primary model encounters timeouts, rate limits, or latency issues.

The backend uses FastAPI to coordinate requests from the React frontend and route them through the sequential rewriting pipeline.

2. The Eight-Dimensional Style Genome

A generic instruction such as "rewrite this in my tone" often produces an exaggerated imitation rather than a useful representation of someone's writing style.

InkFlow instead uses a short linguistic calibration exercise to capture different aspects of how a person writes.

The eight dimensions are:

Formality: The balance between academic language and conversational expression.
Syntactic Density: How frequently subordinate clauses appear in sentences.
Lexical Entropy: Vocabulary diversity and variation.
Burstiness Variance: Variation in sentence length, from short statements to longer, more complex sentences.
Punctuation Signature: The author's use of em-dashes, semicolons, commas, and other punctuation.
Clause Chaining: How the author connects related ideas.
Contraction Ratio: The frequency of forms such as "don't" and "it's."
Tone Affinity: Whether the writing tends to be assertive, exploratory, analytical, or pragmatic.

Together, these dimensions provide a more structured starting point for adapting the writing process to individual preferences.

3. The Four-Stage Sequential Rewrite Pipeline

InkFlow processes writing through four stages.

Stage 1 — Base Draft Generation: Expands raw bullet points or creates a first draft while preserving the original assertions.

Stage 2 — Style Transformation and Slop Removal: Applies the user's Style Genome and removes common synthetic clichés.

Stage 3 — Semantic Fidelity Check: Checks whether the transformed text retains the source's meaning and triggers a retry when significant semantic drift is detected.

Stage 4 — Quirk-Preserving Grammar Pass: Corrects errors while attempting to preserve characteristic punctuation choices and deliberate stylistic patterns.

The final result includes character-level differences so users can inspect the changes instead of blindly accepting a rewritten draft.

4. Engineering Challenges and Solutions

Challenge 1: Preserving Personal Punctuation

Conventional grammar correction can remove em-dashes, semicolons, and other punctuation habits that are part of someone's writing style.

InkFlow separates grammar correction into its own stage and uses deterministic checks to help protect those preferences.

Challenge 2: Preventing Semantic Drift

Aggressive cliché removal can accidentally remove important context, numbers, or factual claims.

InkFlow includes a semantic verification stage designed to detect meaningful divergence and retry when the rewritten text no longer adequately reflects the source.

Challenge 3: Improving the Mobile Writing Experience

A side-by-side editing layout can become difficult to use on smaller screens.

The mobile interface uses a segmented switcher between the source draft and the humanized result, a compact warning strip for detected phrases, and an automatic transition to the result after rewriting.

Challenge 4: Strengthening Authentication

During development, mock authentication tokens created the risk of visitors inheriting a test session instead of signing in with their own accounts.

The production implementation rejects mock tokens, removes frontend fallback tokens, uses Firebase browser-session persistence, and includes an inactivity-based logout policy. The application also allows users to explore drafting features while prompting them to sign in for protected operations.

5. Subscription and Reliability

InkFlow combines Firebase Authentication with a Razorpay-based subscription workflow. Its backend also includes Pydantic request validation, Sentry tracing, and automated tests.

The primary and fallback model arrangement is designed to improve resilience when the primary inference service experiences problems.

These components support the project's broader goal of making personalized writing assistance practical and affordable.

Why Does Open Innovation Matter?

Open innovation matters because writing is personal. People should have more control over how AI transforms their words instead of being forced into a single model's default writing style.

Greater control over writing style: Open-weight models give developers more flexibility to experiment with prompts, model selection, and style-conditioning techniques. This makes it possible to build a writing assistant around individual preferences rather than a generic corporate voice.

More flexible inference: InkFlow uses a primary and fallback model, helping the application respond to model availability and latency problems without relying entirely on one model configuration.

Affordable access: The project combines open-weight inference with a low-cost subscription model intended for students and independent creators.

A focus on authorship: The goal is not to replace a person's ideas with machine-generated prose. It is to help people express their existing ideas more clearly while retaining the characteristics that make their writing recognizable.

InkFlow is an exploration of how open-weight AI can support a more personalized writing experience.

My First User

what my first customer said

reviewfromcustomer.mp4 - Google Drive

drive.google.com

Prize Categories

Based on the project description, these are the partner categories to consider. Include only those for which you can demonstrate the required integration.

Best Use of Render: The FastAPI backend is deployed on Render.
Best Use of Sentry Agent Tracing:

Team Submissions

@soumyaghosh2006 was in my team, help me to develop this product.

My aim with InkFlow is to build writing assistance that adapts to people rather than making people adapt to AI.

Better writing should still sound like you.

Hacktoberfest #DEVChallenge #HF26Challenge #SideHustle #AI #OpenSource #WritingTools

[Boost]

Aishani — Sat, 17 Oct 2026 15:59:32 +0000

Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle Submission 💸

Soumya ghosh

Oct 17

InkFlow: The Adaptive Writing Twin & Anti-AI-Slop Engine

#devchallenge #hf26challenge #ai #opensource

6 min read

Wabi-Sight: The Stone & Cloud Oracle — Nature Micro-Meditations with Gemma 2 & TabPFN

Aishani — Fri, 09 Oct 2026 20:36:34 +0000

🪨 The Stone & Cloud Oracle (Wabi-Sight)

Micro-Meditations from Nature's Shapes — Built for Hacktoberfest 2026: "Touch Grass"

🌿 What I Built — The Real Problem

We live in an era of epidemic screen fatigue. Even apps designed for the outdoors (trail navigators, species classifiers, AR games like Pokémon GO) suffer from the same fundamental flaw: they keep our eyes glued downward to a piece of glowing glass. Hikers walk past ancient canopies while staring at map pins; park visitors miss the rustle of leaves because they are managing digital notifications.

I built The Stone & Cloud Oracle (Wabi-Sight) to solve this exact problem by flipping the role of AI upside down.

Instead of demanding screen time, the Oracle uses open-source multimodal AI to make the screen the shortest, most disposable part of the experience (under 15 seconds). You spot an ephemeral organic pattern outdoors—puddle ripples, dried mud fissures, leaf veins, lichen on stones, or wandering cloud silhouettes—and take a quick snapshot.

The Oracle extracts the pattern's geometry, crafts a poetic metaphor, and then deliberately commands you to turn your phone face down in the grass, close your eyes, and listen to the world around you.

The screen dims into an unhurried breathing circle matching your local circadian rhythm, accompanied by gentle voice guidance and a resonant Tibetan singing bowl chime that tells you when your meditation is complete.

⚡ How It Works — The Core Idea & Technical Approach

The Stone & Cloud Oracle is built with a Dual-Engine architecture engineered to unite cutting-edge open-weight foundation models, tabular machine learning, and screen-minimizing ambient audio.

1. The 5-Step User Journey:

Spot Organic Geometry (< 5s): Spot a fleeting natural pattern on a trail or park.
Consult the Oracle (< 10s): Snap a quick photo with the mobile camera.
Multi-Model Synthesis:
- Prior Labs' TabPFN evaluates local circadian conditions (hour_of_day, temperature, cloud_cover, day_of_year) from a reference ecological dataset to calculate your optimal resonant breathing cadence (e.g., 5.5s resonant rhythm for midday equanimity, 7.0s deep exhale for dusk).
- Google Gemma 2 (Open-Weights) acts as a poetic naturalist (inspired by Bashō, Thoreau, and Mary Oliver), delivering a poetic metaphor, a philosophical reflection, and a tactile somatic grounding command.
- ElevenLabs Voice Synthesis speaks the guidance aloud in an unhurried, serene naturalist voice so you never have to read the screen.
The Screenless Immersion: The screen dims to a tranquil near-black view (#030604). You place the phone face down in the grass, close your eyes, and breathe with the spoken guidance until a Tibetan singing bowl chime marks the end.
Nature Scrapbook: The observation, poetry, and element badge are preserved in an offline field journal.

2. Prize Categories:

Best Use of Gemma ($200 Target): Powered by Google's open-weight Gemma 2 (gemma2-9b-it via Groq Cloud acceleration and local Ollama support). Gemma 2 serves as the poetic brain that transforms raw visual geometries into contemplative literature.
Best Use of TabPFN ($200 Target): We integrated Prior Labs' TabPFN tabular foundation model (pip install tabpfn). TabPFN analyzes ecological weather and seasonal data to dynamically predict circadian respiration cycles.
Best Use of ElevenLabs ($100 Target): Uses ElevenLabs' newest eleven_flash_v2_5 model (Voice ID: "Adam") for realistic documentary-style narration, backed by native browser Web Speech API offline fallbacks.
Best Use of Render ($200 Target): Fully containerized and deployed via a 1-click render.yaml blueprint on Render's free tier.
Best Use of Sentry Agent Tracing ($100 Target): Instruments the pipeline with Sentry spans (tabpfn.forecast, vision.analyze, gemma.synthesize) to monitor end-to-end latency.

3. Why Open Innovation Matters:

100% Offline Trail Independence: Deep forest ravines and national parks rarely have 5G signal. Proprietary cloud APIs fail in the wild; open-weight models run locally on edge hardware with zero external dependencies.
Privacy of Inner Life: Meditative reflections, backyard nature snapshots, and personal breath logs belong solely to the user. No personal biometric or location data is ever mined by ad networks.
Zero Cost Forever: Free open-weights allow anyone, any school outdoor group, and any community nature club to run the software forever at $0.

🎬 Demo — Real Scenarios & Live Links

🚀 Try the Live Web App:

👉 Live Deployed URL: https://stone-cloud-oracle.onrender.com

(Designed mobile-first: open on your smartphone or browser!)

🎥 Video Walkthrough

Watch the Wabi-Sight Demo Video:
(https://1drv.ms/v/c/bbab0edd6634b417/IQDno30x6Th_QJY10OC6etXrAa2IKleXIkxP4uOMRHWGv-k?e=uXjl1E)

🌲 A Real Trail Scenario:

The Spotting: While walking in a park, I spotted concentric ripples in a shallow rainwater puddle.
The Oracle's Revelation: > 🌿 **The Vision: "These ripples mirror the annual growth rings of an ancient cedar, expanding toward the mossy bank." > 🪨 **The Reflection: "Disturbance marks the surface for only a moment; the depth remains undisturbed and still." > 🌬️ **The Grounding: "Place your phone face down on the grass. Close your eyes, feel the sunlight on your eyelids, and take 5 slow breaths."
The Grounding: I set the phone face down in the grass. The screen dimmed completely, soft audio guided my breaths, and a peaceful Tibetan bowl chime sounded at the 30-second mark.
The Result: I spent 10 seconds on my phone and 5 minutes actually listening to the birds and feeling the grass beneath me.

💻 Code — The GitHub Repository

The entire codebase is open-source under the Apache 2.0 license:

👉 GitHub Repository: https://github.com/aisha453/Wabi-Sight

Built with ❤️ for Hacktoberfest 2026. Remember to close your laptop and go touch grass!

TinkerSight: An AI Friend for When You're Stuck With a Device

Aishani — Sun, 04 Oct 2026 05:27:11 +0000

This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend

What I Built

I built TinkerSight, a vision-first AI technical guide for my friend who lives alone and sometimes struggles with unfamiliar appliances and everyday devices.

The problem is simple: when you're standing in front of an unfamiliar AC remote, washing machine, or device, the manual often isn't written for the one question you actually have.

TinkerSight lets someone show a device through a photo, explain what they want to do, and get simple, step-by-step guidance.

Its core idea is:

See → Ground → Simplify → Guide

It doesn't just try to recognize what's in the image. When possible, TinkerSight retrieves relevant official manufacturer documentation and uses it to ground the guidance. If the image is unclear or the situation could be dangerous, it asks for clarification or stops instead of confidently guessing.

I built TinkerSight because I wanted to create something that felt less like searching through a manual and more like having someone standing beside you when you need help.

Demo

Here is a short demo showing three situations:

Using Turbo Cool on a Blue Star AC remote, with the answer grounded in official manufacturer documentation.
Getting guidance for a washing-machine control.
A safety scenario involving a burning smell, where TinkerSight refuses to provide hazardous repair instructions and tells the user to stop.

Demo video:

Watch the TinkerSight Demo video:
https://1drv.ms/v/c/bbab0edd6634b417/IQDjln7aXVbrR5HvUco5p-YwAXIP2aJejHEnnWeCJaIsJGc?e=KX55aw

Code

The complete source code is available on GitHub:

https://github.com/aisha453/TinkerSight

The repository includes the React frontend, FastAPI backend, local AI integration, manufacturer-document grounding, and safety handling.

How I Built It

TinkerSight is built around Qwen3-VL 2B, an open-weight vision-language model running locally through Ollama.

The architecture is:

React + Vite → FastAPI → Ollama → Qwen3-VL 2B

The user uploads an image and describes what they want to do. The vision model first identifies the device, visible controls, brand information, and relevant context.

For supported manufacturers, TinkerSight then retrieves information from selected official manufacturer webpages and uses that information to ground the response.

I also added a deterministic safety layer for obvious hazardous situations such as exposed electrical wiring, gas leaks, smoke, sparks, burning smells, and internal appliance repairs.

One important design principle was: don't pretend to know something that isn't visible or verified.

For example, TinkerSight does not claim an exact appliance model unless it can actually read the model information.

Why Does Open Innovation Matter?

Open innovation made this project possible in a way that would have been difficult to achieve with a closed API alone.

Because TinkerSight uses an open-weight vision-language model through local inference, the AI can run on the user's own machine without sending every image to a proprietary vision API.

It also means the model layer can be replaced or improved without rebuilding the entire application.

More importantly, using open AI made it possible for me to experiment with the behavior of the system itself: how it interprets images, how it handles uncertainty, how manufacturer information is introduced, and when it should refuse to provide instructions.

For a tool dealing with someone's home, appliances, and potentially sensitive images, having the ability to run the AI locally is especially meaningful.

Prize Categories

No partner prize category claimed.

TinkerSight currently uses open-source/open-weight AI and local inference, but I did not add a partner technology solely to qualify for a prize category.

#grinding mode on

Aishani — Fri, 02 Oct 2026 16:01:39 +0000