DEV Community: Analyse with Vanshaj Poonia

There is no Red Tomb: making a 1B model write walking quests without making things up

Analyse with Vanshaj Poonia — Sat, 10 Oct 2026 22:16:45 +0000

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 1: Touch Grass

What I Built

"Go touch grass" is good advice that rarely works, because a walk with no destination loses to the couch every time. What gets people out of the house is a reason: a dog that needs walking, a kid who needs tiring out, a thing to go and see.

Sidequest gives a walk a reason. You tell it three things:

where (your location, or one of four famous parks if you are trying it from a desk),
how long (20 minutes, 40, or an hour),
who is coming (just you, you and a kid, or you and a dog).

It reads OpenStreetMap around you, picks the most interesting real things within reach (a 500-year-old tomb, a statue, a bench with an inscription, a tree someone bothered to name), and routes the shortest loop through them that fits your time. Then Gemma, running on your device, writes you one small thing to do at each stop.

It is for anyone who wants to walk more and keeps not doing it, and for the parent at 4 pm on a Saturday who needs the next 40 minutes to have a plot. The quest is written at home on wifi and saved on the phone, so it still works in the middle of a park with one bar of signal.

Demo

🔗 vanshajpoonia.github.io/sidequest

No sign-up. Pick Lodhi Garden, Golden Gate Park, Hampstead Heath or Central Park, choose a length, and press Make my quest. Or press Start where I am and get a walk around your own block.

The first quest downloads Gemma once (about 860 MB, so use wifi). After that, on my laptop in Chrome with WebGPU, the three-stop quest above took 21 seconds to write, retries included. Every stop has a Walk there ↗ link for directions and a "Why this mission?" panel, which turns out to be the most important part of the app:

That is a real one from the quest above. Gemma's first idea for a radio mast was to climb it. The checker said no, told Gemma why, and the second draft kept you on the ground. The panel shows you all of it.

Code

VanshajPoonia / sidequest

Turns the real map around you into a short walking quest. OpenStreetMap stops, missions written by Gemma 3 running in your browser.

Sidequest 🧭

Turns the real map around you into a short walking quest.

Pick a start and a length (20 minutes, 40, an hour). Sidequest reads OpenStreetMap around you, picks the most interesting real things within reach (a 500-year-old tomb, a blue plaque, the 1894 Liberty Tree, a whispering bench), routes the shortest loop through them that fits your time, and then Gemma, running in your browser, writes you one small thing to do at each stop.

Live: https://vanshajpoonia.github.io/sidequest/

Stop 2 · 🏛️ Viaduct Bridge. Cross the Viaduct Bridge and examine the bridge’s construction – is it solid stone or a cleverly constructed timber?

The model writes. The code navigates.

Given only a place's name, a 1B model fills the gaps with confident inventions (the table below counts them). So Gemma is never asked where to go: choosing stops and adding up distances is arithmetic, and arithmetic belongs in…

View on GitHub

Static files, no build step, no backend, no API keys. MIT.

How I Built It

The model writes. The code navigates.

The first decision was what not to ask Gemma. Picking stops and adding up distances is a job ordinary code does perfectly, so Gemma never chooses where you go. src/plan.js scores OpenStreetMap features by what their tags prove (has a name? an inscription? a Wikipedia link? a date?), keeps one of each kind where it can, and brute-forces the shortest start → stops → start loop, walked at 75 m a minute with a 1.3× allowance for paths not being straight, plus three minutes at each stop. If a loop does not fit your time, you are not offered it.

That leaves Gemma one job, the one a language model is actually good at: turning a list of facts about a place into a sentence a person wants to act on. "Race your kid to the English oak and see if the two of you can reach all the way around its trunk."

The hard part was getting it to do only that.

Measuring it

I took the six best stops around each of the four parks (24 in all, a mix of tombs, statues, plaques, trees, viewpoints and benches), cycled through solo, kid and dog walkers, and wrote a checker that flags a mission if it:

mentions a year or a proper noun that is not in the stop's OpenStreetMap tags,
asks the walker to find out a date the map does not have ("determine the original construction date" turned out to be Gemma's favourite filler),
asks for anything unsafe (climbing, wading, picking, feeding, going inside),
describes the place instead of giving an instruction, or talks to a kid on a dog walk,
runs past 35 words.

Then I ran Gemma 3 1B (4-bit, the exact weights the browser loads) over all 24 stops in four configurations, with greedy decoding so the numbers come out the same on every run.

Prompt	Invented a name or date	Asked for a date the map lacks	Not a mission, or wrong walker	Clean
The place's name only (rules + examples)	15	0	2	8 / 24
OSM tags + rules, no examples	0	0	12	12 / 24
OSM tags + rules + 4 examples as chat turns	4	1	3	16 / 24
+ checker, retry with feedback, template (shipped)	0	0	0	24 / 24

Nothing unsafe and nothing over-long turned up in any configuration, so those columns are left out.

Attempt 1: give it the name and let it use what it knows. This is the obvious prompt, and 15 of 24 missions invented something. Gemma knows of Lodhi Garden and Golden Gate Park, but not well enough to send someone anywhere real, so it fills the gaps with confident detail:

Lion (a statue in Golden Gate Park): "Search the Lion statue in Golden Gate Park for a discarded leash."

Richard Tucker (a bust near Central Park), for a parent and kid: "Search the Red Tomb for your kid's favorite toy, a red rubber ball, and see if it's visible from the entrance."

There is no leash, there is no ball, and there is no Red Tomb in New York. More on the Red Tomb shortly.

Attempt 2: give it the tags and a set of clear rules. The rules worked: no invented names at all. But only half the answers were missions. For a 1B model, rules written in prose are not instructions so much as text to continue, and it often just echoed the input back. Asked for a mission at the Athpula Bridge, it replied "Athpula Bridge". Twice it replied, in full, "Place:".

Attempt 3: show it, do not tell it. I kept the rules and added four worked examples, each as a real turn in the conversation (user: the facts, assistant: the mission) rather than as a paragraph of examples in the prompt. Small models copy the shape of what they have already "said" far more reliably than they follow instructions. Clean missions went from 12 to 16 out of 24.

They also copied things they were never supposed to copy. One of my examples was a made-up place called the Red Tomb, and it started turning up everywhere:

Karl Marx's original grave, Highgate, London: "Count the stones surrounding the Red Tomb and note the faint scent of lavender."

Attempt 4: do not trust it, check it, and tell it what was wrong. What ships runs every draft through the checker. If it fails, the draft goes back to Gemma with the specific reason (It mentions "Red", which is not in the facts. Write the mission again, using only the facts.). After three failures, a plain template takes over: "Find the view, then name the farthest thing you can see." A boring mission beats an invented one.

Result: 24 of 24 clean. Sixteen passed on the first try, five after being told what was wrong, and three fell back to a template.

Check the checker

That Karl Marx line was in my shipped results on the first full run, marked clean. The checker had compared words with a substring search, and the stop's inscription says the remains were buRIED, REmoved and re-inteRRED. As far as the checker could tell, "Red" was right there in the facts.

It now matches whole words, that case is a regression test, and the numbers in the table are from the fixed version.

It was not the only one. Taking the screenshots for this post, I watched the checker reject "Listen carefully to the faint hum emanating from the St. Columba Radio Mast" for "describing the place instead of giving you something to do". The full stop in "St." looked like the end of a sentence. Worse, the draft before it, "Ascend the tower of the St. Columba Radio Mast", had been rejected only by that same accident, because "ascend" was not on the unsafe list. Both are fixed and both are tests now.

I found every one of these bugs the same way: by reading the raw output to choose quotes for this post. I now think that is the most underrated debugging technique in AI work.

What the checker cannot catch

It catches names, dates, unsafe verbs and the wrong audience. It cannot catch an invented ordinary object: at a temple whose tags say nothing about lions, Gemma asked for "the most detailed carving of the lion's head", and that shipped. Some sentences that pass are just odd ("Listen carefully to the plaque"). And Gemma has no sense of occasion. At the spot in Delhi where Indira Gandhi was assassinated, its first draft asked a parent and child to "estimate the exact time of the assassination". The date rule rejected it, but by luck, not judgement.

So the app does not hide any of this. Every stop's "Why this mission?" shows exactly what Gemma was given, links to the OpenStreetMap object, and lists every draft that was rejected and why. If a mission is odd, you can see where it came from.

Choosing the model

Gemma 3 1B instruction-tuned, 4-bit ONNX, through transformers.js on WebGPU, falling back to WASM. About 860 MB, cached after the first visit.
Gemma 3 270M is about a quarter of the size, and in my tests it mostly repeated the prompt back. Too small for this job.
Gemma 4 E2B is the obvious next step up, but its ONNX build is around 3.2 GB, which is too much to ask of a phone browser for a walking app. I did not ship what I could not run on a phone.

Privacy, by construction

"Start where I am" snaps your position to a roughly 500 m grid before asking Overpass for map data, pads the search radius to cover the snap, and does the real distance filtering on the device. The map server learns which neighbourhood you are in, not which house. The AI learns nothing, because the AI is a file on your phone.

Why Does Open Innovation Matter?

Sidequest is two open things working together, and both halves matter.

Open data is what makes the stops real. Every stop is an OpenStreetMap object that a volunteer mapped, often down to the text on a bench plaque. When a mission is wrong because the map is wrong, the "Why this mission?" link takes you to the exact object, and anyone can fix it. That fix then improves every app that uses OSM, not just mine. A closed maps API gives you a rating and a photo carousel, and if it is wrong, you file feedback and wait.

An open model is what makes the walk private and offline. Where you walk, when, and with whom is about as personal as data gets. With Gemma running in the tab, the place facts go into a model on your own device and a sentence comes out. There is no request to an AI service to log, and nothing breaks when the signal does, which in a park is often.

And I could take it apart. Every number in the table above came from running the same weights in Node that the browser downloads, against frozen map snapshots, with greedy decoding. Anyone can run npm test and get the same table. I could compare prompt strategies, swap in a smaller Gemma with one environment variable, and find out exactly where the model needed help. With a hosted model the thing I measured could change next week behind an endpoint, and every stop would cost money forever. Here it costs nothing.

My Agent Session

I built this with Claude Code as a pair programmer. I didn't save the session to DevRelay, so here is the next best thing: the whole evaluation is reproducible.

git clone https://github.com/VanshajPoonia/sidequest
cd sidequest && npm install && npm test

That runs 31 planner and checker checks, then Gemma 3 1B over the 24 stops in all four configurations, and prints the table above along with every failed draft. MODEL=onnx-community/gemma-3-270m-it-ONNX npm test runs the smaller model for comparison.

Prize Categories

Best Use of Gemma: Gemma 3 1B runs entirely in the browser and writes every mission, constrained to OpenStreetMap facts by a measured prompt, a checker and a retry loop.

I turned my grandmother's voice notes into a recipe book, and none of it left my laptop

Analyse with Vanshaj Poonia — Mon, 05 Oct 2026 05:53:49 +0000

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

What I Built

Nani's Kitchen turns a grandparent's rambling voice memos into a printable family recipe book. It runs entirely in a browser tab. No server, no API key, no account, no upload.

I built it for my friend, my grandmother. She has never written a recipe down in her life. What she does is send four-minute WhatsApp voice notes that open with "okay so today I am going to make" and close with an unrelated story about the coal fire.

Those memos are the recipes. They are also the only copy, sitting in a chat backup, one phone replacement away from gone.

Drop a memo in, get back a recipe card with ingredients, method, and the part I actually care about: a section called In her words that keeps the stories instead of deleting them.

Demo

🔗 nanis-kitchen.vercel.app
Mirror: vanshajpoonia.github.io/nanis-kitchen

No sign-up. Load a model, then press Use the sample memo if you have no microphone handy - it drops in a real spoken aloo paratha transcript so you can see the structuring without recording anything.

Watch for the little ▶ buttons on each line. Whisper returns timestamps, so every ingredient and every step replays the exact second she said it. The transcript is a convenience; her voice is the source of truth, and I did not want the app to quietly swap one for the other.

Code

VanshajPoonia / nanis-kitchen

Turn a grandparent's voice memos into a printable family recipe book. Whisper runs in your browser tab; the audio never leaves your laptop.

Nani's Kitchen 🍲

Turn a grandparent's voice memos into a printable family recipe book. Entirely on your own laptop.

My grandmother does not write recipes down. She has never written one down. What she does is send four-minute WhatsApp voice notes that start with "okay so today I am going to make" and end with an unrelated story about the coal fire. Those memos are the recipes. They are also the only copy.

Nani's Kitchen takes those memos, transcribes them with Whisper running inside the browser tab pulls out ingredients, method and the stories, and builds a recipe book you can print and hand over.

No server. No API key. No account. The audio never leaves the machine.

Why it had to be open

This is the part that is not a technicality.

Uploading a recording of your grandmother to somebody's inference API is a thing you only do…

View on GitHub

MIT. Static files, no build step, no runtime dependencies.

How I Built It

Whisper — open weights from OpenAI, Apache-2.0 — running in the tab through transformers.js. WebGPU where the browser has it, CPU/WASM everywhere else, picked at runtime.

flowchart LR
  A[Voice memo<br/>.m4a / mic] --> B[AudioContext<br/>decode + resample 16 kHz]
  B --> C[Whisper<br/>transformers.js<br/>WebGPU / WASM]
  C --> D[Transcript<br/>+ timestamps]
  D --> E{Structurer}
  E -->|offline, instant| F[Rule parser]
  E -->|optional| G[Ollama<br/>any open-weight LLM]
  F --> H[Recipe card]
  G --> H
  H --> I[(localStorage)]
  I --> J[Markdown / printable book]

The interesting file is not the ASR, it is src/recipe.js. Spoken recipes and written recipes are different languages, and every recipe parser I could find assumes the written one. Nobody says "250 g all-purpose flour". They say "about two cups of maida, maybe a bit more if the dough feels dry". So the parser had to learn a few things:

Vague stays vague. "A pinch", "plenty of ghee, do not be shy with it". Converting those to numbers would be inventing data and would make the book worse.
Bare counts are ingredients. "Also four potatoes, medium size, boiled" has no unit anywhere in it, but it is obviously a shopping line, not an instruction.
Descriptors belong to the line above. That same sentence must not become three ingredients called four potatoes, medium size and boiled.
"And then" is a sentence boundary. "Knead it and then cover it" is two steps. My first attempt produced a cooking step that was just the word "And", which is how I learned to stop re-scanning a string I had already matched.
Memories are not instructions, and must not be deleted. "When I was a girl we had no gas, only the coal fire" is the reason you want the book at all.

There are 16 checks in tests/run.mjs covering exactly those cases. They run offline in milliseconds, because the parser is pure functions with no model in it.

There is also an optional path: if Ollama is running, the app finds it, lists whatever open-weight models you have pulled, and uses one instead of the rule parser. Swap llama3.2 for gpt-oss for qwen2.5 and nothing else changes.

Why Does Open Innovation Matter?

Three answers, in increasing order of how much I actually believe them.

The easy one: cost. Transcribing a few hundred old memos through a hosted ASR API is a real bill for something that should be a weekend favour for your family. Here it is zero, permanently.

The better one: it works on the machine it needs to work on. This is going to live on an old laptop in my parents' house, offline half the time. whisper-tiny.en is 40 MB and runs instantly on hardware like that; whisper-small.en is six times bigger and much better at accented English. The right trade-off differs per machine, so it is a dropdown rather than a decision I made for everyone. A closed API gives you whichever model the vendor is serving this quarter.

The one that actually drove the build: uploading a recording of your grandmother is a thing you do exactly once without thinking about it. That audio is her voice, her house, her health, whatever else was audible in the room, and often a language a cloud vendor charges a premium for. With a closed API, "it's private" is a sentence in a terms-of-service document that can be revised. With open weights running in AudioContext and WASM, it is a property of where the code runs — and you can confirm it by opening the Network tab and finding no request to inspect, because there is no request.

Open innovation did not make this cheaper than the closed version. It made a promise checkable that would otherwise have stayed a promise.

My Agent Session

No DevRelay session on this one, so here is the trail instead: the commit history and the test suite.

The parser was not designed up front, it was argued with. Every rule above exists because a test failed first, and tests/run.mjs reads as a changelog of everything I got wrong about how people talk about food:

git clone https://github.com/VanshajPoonia/nanis-kitchen
cd nanis-kitchen && npm test

16 checks, no model, no network. The one I am fondest of is no step is a lone conjunction.

Empowering Artisans in the Digital Marketplace

Analyse with Vanshaj Poonia — Thu, 07 Dec 2023 10:38:57 +0000

Etsy, founded in 2005 by Robert Kalin, Chris Maguire, and Haim Schoppik, has emerged as a global e-commerce platform specializing in handmade, vintage, and unique goods. It has redefined the landscape of online marketplaces by providing a space for independent artisans and crafters to showcase and sell their creations. With a commitment to sustainability and community, Etsy has grown into a unique online marketplace that fosters creativity and empowers small businesses.

Background and History:
Etsy's inception stemmed from the founders' desire to create a platform that connected craftspeople directly with consumers. The initial focus was on handmade goods, but the marketplace evolved to include vintage items and craft supplies. Over the years, Etsy has expanded its reach globally, connecting millions of sellers with a diverse customer base.

Business Model:
Etsy operates on a commission-based business model, charging sellers a nominal fee for listing items and a percentage of each sale. This model incentivizes the platform to support its sellers, as Etsy's success is directly tied to the success of its community. The platform also generates revenue through optional seller services such as advertising and payment processing.

Unique Selling Proposition (USP):
Etsy's USP lies in its commitment to fostering a sense of community and supporting small businesses. Unlike conventional e-commerce giants, Etsy focuses on niche markets, providing a platform where buyers can find distinctive, handmade, and personalized items. This emphasis on uniqueness and craftsmanship has helped Etsy carve out a distinct identity in the competitive e-commerce space.

Technology and Innovation:
Etsy has continually invested in technology to enhance the user experience for both buyers and sellers. The platform leverages machine learning algorithms to improve search and recommendation features, helping users discover relevant products. Additionally, Etsy has embraced mobile commerce, ensuring that users can access the platform seamlessly through mobile apps.

Community Building and Support:
One of Etsy's standout features is its emphasis on community building. The platform facilitates communication between buyers and sellers, enabling direct interaction and personalized customer service. Etsy also organizes events and forums, both online and offline, to connect sellers and share knowledge. This sense of community fosters trust and loyalty among users.

Challenges and Controversies:
While Etsy has experienced tremendous success, it hasn't been immune to challenges and controversies. Issues such as counterfeit goods, copyright infringement, and concerns about the platform losing its handmade focus have arisen. Etsy has responded by implementing stricter guidelines, deploying advanced algorithms to detect policy violations, and refining its policies to maintain the integrity of the marketplace.

Sustainability and Ethical Practices:
Etsy places a strong emphasis on sustainability and ethical practices. The company has committed to offsetting carbon emissions from its shipping activities and has implemented initiatives to reduce its overall environmental impact. This commitment to sustainability aligns with the values of many artisans and resonates with environmentally conscious consumers.

Global Impact and Expansion:
Etsy's impact extends beyond its platform, influencing the broader creative economy. By providing a global stage for artisans, Etsy has contributed to the economic empowerment of individuals worldwide. The platform's expansion into international markets has facilitated cross-cultural exchange and enabled small businesses from various regions to connect with a diverse customer base.

Financial Performance:
Etsy's financial performance has reflected its success in the e-commerce sector. The company's revenue has seen consistent growth, driven by an increase in active buyers and sellers. Strategic acquisitions, such as the purchase of Reverb, a musical instrument marketplace, have further diversified Etsy's portfolio and contributed to its financial resilience.

Future Outlook:
As Etsy continues to evolve, it faces challenges and opportunities in an ever-changing e-commerce landscape. The platform's future success depends on its ability to balance growth with maintaining its commitment to handmade and unique goods. Innovations in technology, sustainability practices, and community engagement will likely play pivotal roles in shaping Etsy's trajectory.

Conclusion:
Etsy's journey from a small online marketplace to a global e-commerce giant exemplifies the power of connecting artisans with consumers. The platform's unique focus on handmade, vintage, and personalized items has created a niche that resonates with a growing segment of conscientious consumers. Etsy's commitment to community, sustainability, and innovation positions it as a key player in the digital marketplace, with the potential to continue empowering independent creatives around the world.

Oracle Corporation - A Case Study on Success Factors

Analyse with Vanshaj Poonia — Mon, 20 Nov 2023 09:49:44 +0000

Oracle Corporation, founded in 1977 by Larry Ellison, Bob Miner, and Ed Oates, has emerged as one of the world's leading technology companies, specializing in database management systems, cloud solutions, and enterprise software. This case study delves into the key factors that have contributed to Oracle's remarkable success.

Innovation and Technology Leadership:

Oracle has consistently been at the forefront of technological innovation. The company's early success can be attributed to the development of the Oracle Database, which revolutionized the field of relational databases.
Continuous investment in research and development (R&D) has allowed Oracle to stay ahead of the curve. The company's commitment to cutting-edge technologies, such as artificial intelligence, blockchain, and cloud computing, has helped it maintain a competitive edge.

Comprehensive Product Portfolio:

Oracle offers a diverse range of products and services, catering to various business needs. The company's comprehensive product portfolio includes database management systems, cloud infrastructure, enterprise applications, and business analytics solutions.
The breadth and depth of Oracle's offerings make it a one-stop solution for businesses, allowing them to integrate various software and hardware components seamlessly.

Strategic Acquisitions:

Oracle has strategically pursued acquisitions to expand its product and service offerings. Notable acquisitions include Sun Microsystems, PeopleSoft, BEA Systems, and NetSuite.
These acquisitions have not only broadened Oracle's product portfolio but also allowed the company to integrate new technologies and talent, strengthening its overall position in the market.

Global Presence and Customer Base:

Oracle operates on a global scale, serving customers in virtually every industry. The company's expansive international presence has allowed it to tap into diverse markets and cater to the needs of a wide range of businesses.
Building and maintaining strong relationships with a large and loyal customer base has been a key factor in Oracle's success. The company's commitment to customer satisfaction and support has fostered long-term partnerships.

Adaptability and Cloud Transition:

Recognizing the industry shift towards cloud computing, Oracle has successfully transitioned its business model to focus on cloud services. The Oracle Cloud Infrastructure (OCI) offers a scalable and secure cloud platform for businesses.
The adaptability to market trends and the successful execution of cloud initiatives have positioned Oracle as a major player in the cloud computing space.

Strong Leadership and Corporate Culture:

Larry Ellison, Oracle's co-founder and long-time leader, has played a pivotal role in the company's success. His visionary leadership and strategic decision-making have shaped Oracle's trajectory.
Oracle's corporate culture emphasizes innovation, collaboration, and a commitment to excellence. The company fosters an environment that encourages employees to think creatively and stay at the forefront of technology.

Focus on Security and Compliance:

Given the critical nature of the data managed by Oracle's products, the company places a strong emphasis on security and compliance. Oracle databases are known for their robust security features, addressing the growing concerns around data protection.
Adhering to industry regulations and proactively addressing security challenges has enhanced Oracle's reputation as a trusted provider of enterprise solutions.

Summary:

Oracle Corporation's success can be attributed to a combination of innovation, strategic acquisitions, a comprehensive product portfolio, global reach, adaptability to market trends, strong leadership, and a customer-centric approach. By staying at the forefront of technology and continuously evolving its offerings, Oracle has maintained its position as a leader in the highly competitive tech industry.