Dial in and you get a full Windows 95 desktop. Minefield, Painter, ScanDisk, a screensaver, mail from your dad, a chat room, an encyclopedia.
The Dial-Up Networking dialog has the "ask others not to use the telephone" checkbox. It nagged you and did nothing, same as it did then.
Real handshake recording, everything else synthesized - including throttling that actually pulls bytes at simulated 56k speed. Handshake staging follows the real ITU specs (V.34, V.8).
Full Win95 desktop once you’re “online” - Find Files, Recycle Bin, Minefield, Painter, an ISP mail client, a chat room, a hand-written encyclopedia parody. All client-side, nothing scraped.
Favorite detail: the “ask others not to use the phone” checkbox in Dial-Up Networking is real 95 behavior - it didn’t stop anything, it just nagged you, because picking up the handset actually dropped the carrier.
Remember clicking a link on somebody’s blog, ending up on a site about nothing, clicking again, and losing an hour with nothing deciding what came next? No feed, no for-you page, no algorithm. Just a stranger’s weird page about lighthouses or old computers or whatever.
I missed that, so I made a button. You press it, you land somewhere. Everything behind it was found and written up by hand, one at a time.
Been thinking about how the internet used to work. You’d click a link on someone’s blog, end up on a site about nothing, click again, and lose an hour without an algorithm deciding any of it for you. Nobody was optimizing your attention. Stuff was just out there.
So I put together a button. Press it, it drops you somewhere - a one-joke site, a stupid little toy, somebody’s personal page about a topic they will not shut up about. 101 places, all picked by hand.
No account, no feed, no cookies, nothing saved. Just the button.
Been thinking about how the internet used to work. You’d click a link on someone’s blog, end up on a site about nothing, click again, and lose an hour without an algorithm deciding any of it for you. Nobody was optimizing your attention. Stuff was just out there.
So I put together a button. Press it, it drops you somewhere - a one-joke site, a stupid little toy, somebody’s personal page about a topic they will not shut up about. 101 places, all picked by hand.
No account, no feed, no cookies, nothing saved. Just the button.
When North came out in ’94, critics torched it for being mean-spirited, and the director thought he was making a heartwarming “appreciate your family” fable.
Rewatching it through an estrangement lens hits completely differently. What the filmmakers meant as a silly lesson plays out like a masterclass in how people react when you declare yourself a free agent and walk away:
The neglect is “not bad enough”: His parents aren’t monsters. They’re distracted, self-absorbed, indifferent. The film frames that as too minor to justify leaving, which is exactly how estrangement gets dismissed when you can’t point to one dramatic incident.
Weaponized guilt: The moment North sets a boundary and leaves, his parents fall into catatonic comas from “grief” and get put in a museum display to shame kids. Classic “look what you’re doing to us by leaving.”
Children as property: Every substitute family treats him like an asset or a trophy. The Texas family wants a capitalist status symbol, Hawaii uses him as a tourism ad. Not an actual human being.
No opt-out: He’s given a deadline. Pick a family by Labor Day or a committee assigns him one. Being unattached is simply not on the menu.
Hollywood pulled its punches with the cop-out dream ending because ‘90s media refused to let anyone stay gone. But that core impulse - a kid realizing he isn’t seen or valued, and choosing to walk - hits remarkably close to home.
Game Title: Salvage Run
Playable Link: https://salvage-run.vercel.app/
Platform: Web (desktop and mobile browser)
Description:
Salvage Run is a push-your-luck game about scavenging a collapsed city. A run is six sites, each one deeper in and worth more than the last. At every site you pick from three options with the payout and the survival odds printed on them: something picked over at 87%, something unstable at 66%, or a hot zone at 45%.
Clear a site and you face the only real decision in the game, which is whether to carry what you have back behind the wire or push further in. Get caught and everything you have not banked stays out there.
The twist is the nightly quota. Meridian Camp sets a number, and coming back under it counts as short even if you walked out clean. That means playing safe is not automatically correct. Some nights the safe route cannot reach the number and you have to take a bad site on purpose.
Runs generate deterministically from a seed, so finishing gives you a link that reproduces your exact map for someone else. Identical sites, identical odds, identical rolls, with your score sitting there to beat. There is also a daily map that is the same for everyone, one attempt.
A run takes about ninety seconds. No account, no install, no ads.
Free to Play Status:
- [x] Free to play
- [ ] Demo/Key available
- [ ] Paid (Allowed only on Tuesdays with [TT] in the title)
Involvement:
Solo project, built after hours. The design, balance, writing, SVG art, and the procedural Web Audio score are all mine. I build with AI coding assistance for implementation.
What I would like feedback on:
- Does the bank-or-push decision start to feel hard by site three, or are you
pushing every time on autopilot?
- Is the quota doing anything for you, or is it noise?
- If someone sent you a challenge link, would you send one back?
Happy to play and comment on anything posted here in return.
Built this over the last stretch as a side project. You answer yes/no
questions about yourself, then guess what percent of everyone else says
yes to the same thing. Go through a small deck, get a results summary,
share a spoiler-free emoji card at the end.
No accounts, no payments, nothing stored beyond a local flag that
remembers which questions you've played.
Stack: Vite/React, Vercel functions, Upstash Redis for the tallies.
Would love feedback on the questions themselves, whether the guessing
part feels fun vs. just a quiz, and anything that breaks.
Game Title: Vox Pop
Playable Link: https://voxpop-delta.vercel.app/
Platform: Web (Optimized for Mobile Web)
Description:
Vox Pop is a minimalist, zero-friction web game centered around human intuition and crowd psychology. The core concept is straightforward: you are presented with a series of simple, everyday binary questions. After answering for yourself, the real game begins: you use a slider to guess exactly what percentage of the collective crowd answered "Yes."
The app immediately compares your guess against live data from real players, scoring you on your accuracy and showing your margin of error. I built this because I wanted a lightweight daily routine game that completely cut out modern web friction. There are no accounts to create, no app store downloads required, and zero intrusive ads. It is designed to be a clean, high-contrast, mobile-first experience that you can play through in about a minute.
Free to Play Status:
[X] Free to play
[ ] Demo/Key available
[ ] Paid
Involvement: Solo developer. I designed the UI, built the front-end, and set up the serverless backend data layer.
Working on a document generation tool where an LLM outputs LaTeX for a single-page layout. The issue is that the model doesn't reliably know what "fills exactly one page" means, so early outputs are either way too sparse or spill onto a second page.
Tried counting content elements and total characters in the generated body to set floors and ceilings, calibrating those thresholds using a different model's output as a stand-in. But when testing the actual production model, it barely hit the character floor, with element density at about half of what was assumed. Because different models structure their output differently, heuristics don't transfer well.
Currently running a measure-and-repair loop: generate, score against floor/ceiling thresholds, and if it's out of bounds, run a corrective pass to expand or compress, keeping the higher-scoring version. Also branching the initial prompt based on the density of the source data, telling it to expand upfront if the input is light.
Still tuning the thresholds against live traffic. Anyone else doing structured, single-page generation find a cleaner proxy for "fullness" than raw character or element counts? Feels like there should be a better signal.
Working on a document generation tool where an LLM outputs LaTeX for a single-page layout. The issue is that the model doesn't reliably know what "fills exactly one page" means, so early outputs are either way too sparse or spill onto a second page.
Tried counting content elements and total characters in the generated body to set floors and ceilings, calibrating those thresholds using a different model's output as a stand-in. But when testing the actual production model, it barely hit the character floor, with element density at about half of what was assumed. Because different models structure their output differently, heuristics don't transfer well.
Currently running a measure-and-repair loop: generate, score against floor/ceiling thresholds, and if it's out of bounds, run a corrective pass to expand or compress, keeping the higher-scoring version. Also branching the initial prompt based on the density of the source data, telling it to expand upfront if the input is light.
Still tuning the thresholds against live traffic. Anyone else doing structured, single-page generation find a cleaner proxy for "fullness" than raw character or element counts? Feels like there should be a better signal.
Working on a tool that refines and structures messy, user-provided text to match a specific target (like aligning a raw project draft to a technical specification). The core challenge isn't the formatting - it's getting the model to stop "improving" the truth.
In early runs, if a user's raw input said they "assisted with a data migration," the LLM would occasionally output that they "successfully led a migration of 10M records." It’s a classic hallucination problem, but with a subtle twist: it's not generating total gibberish; it's just mildly exaggerating to satisfy the prompt's instruction to "make this highly compelling.
Tried a few things to anchor it:
The "Guilty Until Proven Innocent" verification step: Running a secondary LLM call after the generation that does nothing but fact-check the output against the raw input. If it finds a claim not supported by the source, it flags it. Works, but adds latency and doubles API costs.
Strict negative prompting + JSON schema pairing: Forcing structured JSON output with a specific factual_justification field for every single change. If the model has to explicitly map every optimized point back to a raw quote in the input, the exaggeration drops significantly.
The "Diff" approach: Instead of letting the model rewrite the whole block, forcing it to output only the specific edits or words to change. Much easier to control, but kills some of the natural flow of the rewrite.
Currently using a mix of #2 (strict schema mapping) and a lightweight validation script. It's down to a manageable level, but still requires constant vibe-checking.
How are others handling this "creative slippage" when doing LLM-assisted editing or summarization? Are you solving it with strict prompting, secondary arbiter models, or something on the parsing/diff side?
Small feature, more interesting to build than expected. The tool takes pasted user input and sends it to an AI model. Someone reviewing the privacy posture asked the obvious question: what stops someone from pasting an SSN in there.
Answer, before this week: nothing.
Fixed it by pattern-matching common SSN formats server-side and rejecting the submission with a 400 before the API call ever fires, plus a client-side mirror of the same check so people get instant feedback instead of waiting on a round trip.
Deliberately scoped to just SSNs, not a general PII scrubber - a broad "block anything that looks like personal data" pass would wreck a tool whose entire input relies on personal context and detailed text to function.
Same week, added country gating using the platform's edge-provided IP-country header to reject non-US traffic outright, since the product isn't ready to reason about international regulations or regional data handling yet.
Neither is a hard technical problem on its own. What was interesting was realizing how much of "responsible AI feature" work is actually just deciding what NOT to send upstream, before the model ever gets involved.
Anyone else drawing that same line - screen hard before the LLM call rather than trying to constrain what the model does with sensitive input after the fact?
Campbell’s monomyth theory: myths from cultures with zero contact keep landing on the same structure - a call, a threshold, an ordeal, a return.
The threshold stage is the interesting one. Passage almost never comes down to just courage - it requires something specific. Osiris passes through the gates of the Duat by naming each one correctly. Aeneas needs a literal golden bough to enter the underworld, bravery alone doesn’t open the door. Orpheus is let past death itself, on one condition he can’t keep.
None of these cultures were in contact. Campbell’s read: real thresholds in a life work the same way - it’s rarely about willpower, it’s about finding the one specific thing that actually lets you through.
Built a small tool that draws a random myth and tags it to its stage (Call/Threshold/Ordeal/Return) with a reading applied to your question: https://hero-compass.vercel.app/. Only 5 of 20 myths are ones Campbell discussed directly himself - rest apply the structure to myths he didn’t, and the app’s upfront about which is which.
Curious where people think the pattern gets stretched too thin.
Marie-Louise von Franz was Jung's closest collaborator on fairy tale interpretation, working with him from 1933 until his death in 1961, and wrote something like 20 books applying his framework to folklore. Her method rests on a claim worth sitting with: fairy tales aren't diluted myths or children's stories, they're the purest expression of the collective unconscious, precisely because they're stripped of the cultural specificity that muddies myth. A story about Zeus carries Greek theology with it. A story about a king with three sons carries almost nothing but structure, which is exactly why the same structure shows up across a hundred cultures with a hundred different names for the characters.
Her preferred teaching example was The Three Feathers, a minor Grimm tale most people have never heard of, and picking it was deliberate. A king can't decide which of his three sons should inherit the throne, so he blows three feathers into the wind and tells each son to follow wherever his lands. The two "capable" sons get sent toward impressive destinations. The one everyone calls Simpleton watches his feather sink straight into the ground, follows it into a hole in the earth, and meets a talking toad.
Von Franz's reading: the king represents a psyche gone rigid and one-sided, a logical, linear masculine principle that's run out of room to grow. The feathers are thoughts and fantasies drifting up out of the unconscious, genuinely useful if you're willing to follow one instead of arguing with it. The toad underground is the neglected feminine principle (the anima, in Jung's term) forced into hiding by a court with no place for the intuitive or irrational, quietly outproducing the "sensible" plans of two brothers who never think to look down.
The detail I keep coming back to: the feather-throwing itself wasn't invented for the story. Casting something to the wind and following where it lands was an actual, practiced method of consulting fate, and more than one scholar draws a straight line from it to the coin toss underneath the I Ching. Von Franz was teaching Jungian psychology through a story that already contained a real divination method inside it, whether or not that was the original point of the tale.
Got interested enough in this that I built a small tool applying the same four-part lens (shadow, anima, animus, self) across a library of fairy tales, if anyone wants to poke at it: https://vonfranz.vercel.app/. More interested in whether the archetype tagging holds up to people who know this material better than I do than in traffic to it.
Hey everyone, I built a minimal web app for I Ching readings. I wanted something fast with zero friction - no signup, no ads, just the core 64 hexagrams using the classical Wilhelm-Baynes translation.
I also added an optional AI interpretation layer to give a plain-language reading connected to your specific question, though the classical text always shows up first, fully intact.
Built it to scratch a personal itch and keep the tool completely accessible. Check it out here if you want to try it: https://iching-silk.vercel.app/
Would love any feedback on the UI or the AI layer!
Classical Wilhelm-Baynes translation, three coin method, 64 hexagrams. Takes 60 seconds, no signup, no ads.
It also has an AI interpretation layer if you want a plain-language reading connected to your question - but the classical text is always shown first, fully intact.
Built it because I wanted something that treated the tradition seriously.
Here if anyone wants to try it: https://iching-silk.vercel.app/
Classical Wilhelm-Baynes translation, three coin method, 64 hexagrams. Takes 60 seconds, no signup, no ads.
It also has an AI interpretation layer if you want a plain-language reading connected to your question - but the classical text is always shown first, fully intact.
Jung himself drew on the I Ching - it influenced his thinking on synchronicity and the unconscious.
Here if anyone wants to try it: https://iching-silk.vercel.app/