From fee0e643e4062902896ad6a621f269c350dafb2e Mon Sep 17 00:00:00 2001 From: nobody Date: Thu, 30 Oct 2025 16:26:48 -0700 Subject: [PATCH] feat: add multiple models --- python3/salience/__init__.py | 18 +- python3/salience/salience.py | 47 ++++- python3/salience/static/index.html | 107 ++++++++--- python3/transcript-1.txt | 91 +++++++++ python3/transcript.txt | 286 +++++++++++++++++++++++++++++ 5 files changed, 517 insertions(+), 32 deletions(-) create mode 100644 python3/transcript-1.txt create mode 100644 python3/transcript.txt diff --git a/python3/salience/__init__.py b/python3/salience/__init__.py index 9adf40a..8c83ade 100644 --- a/python3/salience/__init__.py +++ b/python3/salience/__init__.py @@ -1,18 +1,30 @@ -from flask import Flask +from flask import Flask, request import numpy as np -from .salience import extract +from .salience import extract, AVAILABLE_MODELS import json app = Flask(__name__) with open('./transcript.txt', 'r') as file: source_text = file.read().strip() -sentence_ranges, adjacency = extract(source_text) + +@app.route("/models") +def models_view(): + return json.dumps(list(AVAILABLE_MODELS.keys())) @app.route("/salience") def salience_view(): + model_name = request.args.get('model', 'all-mpnet-base-v2') + + # Validate model name + if model_name not in AVAILABLE_MODELS: + return json.dumps({'error': f'Invalid model: {model_name}'}), 400 + + sentence_ranges, adjacency = extract(source_text, model_name) + return json.dumps({ 'source': source_text, 'intervals': sentence_ranges, 'adjacency': np.nan_to_num(adjacency.numpy()).tolist(), + 'model': model_name, }) diff --git a/python3/salience/salience.py b/python3/salience/salience.py index 27c7316..2af1238 100644 --- a/python3/salience/salience.py +++ b/python3/salience/salience.py @@ -13,9 +13,45 @@ NLTK_DATA_DIR = os.path.join(PROJECT_DIR, 'nltk_data') nltk.data.path.insert(0, NLTK_DATA_DIR) # Download to the custom location -nltk.download('punkt', download_dir=NLTK_DATA_DIR) +# Using punkt_tab (the modern tab-separated format introduced in NLTK 3.8+) +# instead of the older punkt pickle format +# The punkt_tab model version depends on the NLTK Python package version +# Check your NLTK version with: uv pip show nltk +nltk.download('punkt_tab', download_dir=NLTK_DATA_DIR) + +# Available models for the demo +AVAILABLE_MODELS = { + 'all-mpnet-base-v2': 'all-mpnet-base-v2', # Dec 2020 + 'gte-large-en-v1.5': 'Alibaba-NLP/gte-large-en-v1.5', # Jan 2024 + # 'qwen3-embedding-4b': 'Qwen/Qwen3-Embedding-4B', # April 2025 + 'mxbai-embed-large-v1': 'mixedbread-ai/mxbai-embed-large-v1', +} + +# On clustering +# mixedbread-ai/mxbai-embed-large-v1: 46.71 +# gte-large-en-v1.5: 47.95 +# Qwen/Qwen3-Embedding-0.6B: 52.33 +# Qwen/Qwen3-Embedding-4B: 57.15 + +# On STS +# gte-large-en-v1.5: 81.43 +# Qwen/Qwen3-Embedding-0.6B: 76.17 +# Qwen/Qwen3-Embedding-4B: 80.86 +# mixedbread-ai/mxbai-embed-large-v1: 85.00 + +# Load all models into memory +print("Loading sentence transformer models...") +models = {} + +models['all-mpnet-base-v2'] = SentenceTransformer('all-mpnet-base-v2') +print("Loading Alibaba-NLP/gte-large-en-v1.5") +models['gte-large-en-v1.5'] = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True) +#print("Loading Qwen/Qwen3-Embedding-4B") +#models['qwen3-embedding-4b'] = SentenceTransformer('Qwen/Qwen3-Embedding-4B', trust_remote_code=True) +print("Loading mixedbread-ai/mxbai-embed-large-v1") +models["mxbai-embed-large-v1"] = SentenceTransformer('mixedbread-ai/mxbai-embed-large-v1') +print("All models loaded!") -model = SentenceTransformer('all-mpnet-base-v2') sent_detector = nltk.data.load('tokenizers/punkt/english.pickle') def cos_sim(a, b): @@ -40,7 +76,8 @@ def get_sentences(source_text): sentences = [source_text[start:end] for start, end in sentence_ranges] return sentences, sentence_ranges -def text_rank(sentences): +def text_rank(sentences, model_name='all-mpnet-base-v2'): + model = models[model_name] vectors = model.encode(sentences) adjacency = torch.tensor(cos_sim(vectors, vectors)).fill_diagonal_(0.) adjacency[adjacency < 0] = 0 @@ -51,9 +88,9 @@ def terminal_distr(adjacency, initial=None): scores = sample.matmul(torch.matrix_power(adjacency, 10)).numpy().tolist() return scores -def extract(source_text): +def extract(source_text, model_name='all-mpnet-base-v2'): sentences, sentence_ranges = get_sentences(source_text) - adjacency = text_rank(sentences) + adjacency = text_rank(sentences, model_name) return sentence_ranges, adjacency def get_results(sentences, adjacency): diff --git a/python3/salience/static/index.html b/python3/salience/static/index.html index 7b11566..7bdaa39 100644 --- a/python3/salience/static/index.html +++ b/python3/salience/static/index.html @@ -1,4 +1,4 @@ -CTYPE HTML> + @@ -36,6 +36,23 @@ CTYPE HTML> font-weight: normal; color: #a0a0a0; } + .controls { + width: 700px; + margin: 15px auto; + font-family: sans-serif; + } + .controls label { + margin-right: 10px; + color: #4d4d4d; + } + .controls select { + padding: 5px 10px; + font-size: 14px; + border: 1px solid #ccc; + border-radius: 4px; + background-color: white; + cursor: pointer; + } span.sentence { --salience: 1; background-color: rgba(249, 239, 104, var(--salience)); @@ -51,16 +68,27 @@ CTYPE HTML>

Salience - automatic sentence highlights based on their significance to the document + sentence highlights based on their significance to the document

+
+ + +

- diff --git a/python3/transcript-1.txt b/python3/transcript-1.txt new file mode 100644 index 0000000..3f6d812 --- /dev/null +++ b/python3/transcript-1.txt @@ -0,0 +1,91 @@ +Social reality might not be a video game, but there’s no point trying to imagine that. Crass realism obscures the rules. Besides, society converges upon a video game – or immersive ludic simulation – even if it isn’t one already. Such gamification is a trend to note. It has multiple drivers. + +As games get more convincing, they increasingly set the default perceptual frame. In technologically-advanced societies, game-like systems are becoming the obvious model for self-understanding. The reception for stories with this slant continuously improves. Even scientific theorizing is drawn to them. The topic might seem less than serious, even definitively so, but ultimately it isn’t. Alternatively, it might be said that there is a non-seriousness more serious than seriousness itself. Everything will be gamified. + +In the epoch of WMD deterrence, unlimited warfare is not allowed to happen. Instead, it is perpetually simulated. Every serious military establishment becomes a set of war-games in process. From the peak of virtual thermonuclear spasm, war-gaming cascades down through the apparatus of conventional war-fighting capability, and then spreads outwards – like a blast-wave – through every civilian forum of institutional planning. Eventually (but already) to have been ‘war-gamed’ just means to have been thought through. A war-game is less serious than a war, but it’s the most serious way to process things when war is off the table. It’s also – from its inception – the way to keep war off the table. Si vis pacem, para bellum, which means playing it out. + +That everything would be gamified was decided during the pre-history of computing, at the latest. The potential to simulate anything, which is only to say emerging artificial intelligence, leaves nothing that cannot be folded into a game, given time. + +In their take-off phase, at least, machines demand strict rules, responding well only to precise instructions. They dissipate fog or, more precisely, motivate its dissipation. The world adjusts to machine intelligence by sharpening its definition. Formalization acquires precise practical criteria. + +Anything that trains an AI has to function as a game. This is because playing games is the only thing AI can ever do. For synthetic intelligence to be applied to a problem, of any kind, it has to be gamified. Then strategies can be pursued, in strict compliance with rules, to maximize success. Optimization games are the only kind that exist, and inversely. + +While games are made, or adopted, for AI to play in, games incorporate AIs into themselves, as components. Simply making games that work requires computer game companies to nurture a semi-independent machine intelligence lineage of their own. Playing against AIs, and also alongside them, is ever increasingly what gamers do. This is what the ‘single player’ option abbreviates, most obviously. The antisocial path stimulates nonlinearity on the side of the machine. Machine intelligence escalation twists into an ever tighter loop, continually intensifying, as it plays games against itself, and against anyone else who wanders in to challenge it. + +The games that are relentlessly improving – the kind ‘gamers’ play – are competition for society. They provide an alternative to traditional modes of social involvement. Japanese ‘otaku’ pioneered these paths of departure. Wherever technology crests, the world follows them. Advance tends to exit. + +‘Incel’ – or ‘involuntary celibate’ – is in some ways a misleading term for what is happening here. The condition of fundamental social alienation described is no more ‘involuntary’ than any other opt-out. The ‘incel gamer’ no longer finds the most basic of all traditional social relations worth it. There are better games. The revealed preference is evident regardless of what might be said. They grasp games as a way to leave. + +At the same time, the PUAs – or ‘pick-up artists’ – have been pulling everything apart from the opposite direction. If they have a bible it is Neil Strauss’s The Game. Rather than abandoning mating for games, the PUAs gamify mating. + +Turning it into a game is the first step to becoming good at it. In the same way, war is ‘the game of princes’. Everything is a game to those who are good at it, and as a condition of them coming to be good at it. This is the serious non-seriousness previously touched upon. Excellence has ludic foundations. Play or be played, as it is cynically said. + +How could it not become ever more obvious that ‘Gamergate’ had to happen? If non-Wokeness in the gaming industry had never been an issue, it would be a sign that nothing of importance was taking place there. In reality, it could not be left alone because it was destined to eat everything. The topic was seriously non-serious, as the GameStop short-squeeze was more recently. + +Good or well-constructed games have a number of characteristic features. + +Firstly, they can only be played by the rules. Cheating is forbidden less than it is made impossible. Physics is like this. It proscribes nothing that can be done (as Crowley notoriously noticed). Rules that can be broken are a failure of game design. The more impractical it is to cheat, the better the game. + +Secondly, they have an implicit meta-rule that strictly prohibits changing the rules. To change the rules is to invent a new game, which cannot be done during play. Different games, with different rules, coexist simultaneously, rather than replacing each other successively. + +Thirdly, rule sets permit outcomes, without ever dictating them. Rules and strategies are mutually independent. Strategies compete within the rules, rather than over them. Strategic modification of rules, or the adaptation of rules to strategy, is essentially corrupt. + +Fourthly, each is fully enveloped by some consistent incentive structure. This renders success and failure unambiguous, grading performance. The players always know how it went. + +The ‘games’ favored by game theorists, such as variants of the prisoner’s dilemma, compose a small subset of such well-constructed games. They cannot be transcended by cheating. Game modification is never a permitted move. They permit no legislative power. Each has a single reward dimension. + +The breadth of application suggests these constraints are not difficult to meet. It might even seem that any alternative to a well-constructed game is anomalous in its degeneracy. + +To be a progressive is to be in favor of changing the rules. There is one ‘arc of history’ and it is made of reforms. Old rules and structures of oppression are considered broadly identical. + +A conservative is against changing the rules. If they are changed, they stay changed, because changing them back would still count as change. Thus the much derided function of conservatism as anchor for the progressive ratchet. + +A reactionary holds that the rules should never have been changed. Reaction would delight in restoring old rules, were it ever in a position to do so. It never is, and will never be. + +A neoreactionary accepts experimental variation in rules only when rule sets are multiplied. New rules are to be tolerated only alongside, in addition to, and as a concurrent alternative to old rules. They are legitimated only by hard forks. Anything else is progress, which is in all cases misfortune. + +Progress is reform without schism. While wrapping itself in the mantle of science, it incarnates a drastic violation of scientific method. Positive or negative characterizations of ‘progressive experiments’ are equally misleading. Progressive change is not experimental, but rather something closer to the opposite. It substitutes for testing, and disdains controls. Synchronic comparison is deliberately suppressed, and the more thorough the suppression the more progressive it is. Multiplication without difference is bad, but difference without multiplication is worse. + +In a corrupt society, or bad social game, the ruling class makes rules. There is nothing natural about this, regardless of what we are told. It is only in the wake of a radical socio-cultural calamity that it happens. + +In any well-constructed game, winning is entirely distinct from re-writing the rules. For instance, a speculative investor – however successful – does not modify the functioning of the stock market, any more than a chess master takes advantage of each victory to change the way pieces move. + +Capitalism, as a game, works well when businesses follow economic rules they have no role in formulating. Even in the political sphere, comparatively stable constitutional principles and norms are expected to conserve themselves resiliently through vicissitudes of party conflict. This point might confidently be strengthened. Invulnerability of political rules-of-the-game to party fortune is regime stability. The contrary condition, in which party dominance overwhelms political rules and permits the dictation of new ones, defines revolution. Competition within rules is politics, but competition to set rules is war. When politics seems more like war than it used to, this is why. + +The common law tradition permits no legislation. Laws are discovered, never made. The notion of law-making is abominable, and inconsistent with the existence of a free people. According to the only truly English position, legislation is always and essentially tyranny. + +Optimally, the rule of law is a pleonasm. It means only that the rules rule. Nothing could be more inevitable. + +‘Algorithmic governance’ says roughly the same. Yet under conditions of fundamental social corruption the ‘rule of law’ appears closer to an oxymoron. Is it not always men who in fact rule, with rules as their instrument? If so, formal procedure is mostly mystique. Yet this question is itself an index of decadence. Only when a game is already broken does it appear so lacking in authoritative constraint. + +America is a game so badly broken the world is positively awe-struck by it. Its hegemony ensures that everyone has to care. Most of the planet finds itself sucked into a game whose formal rule set is a chaotic cancerous mess. + +When America had a frontier, it was a land of real experiments. New games of all kinds were explored, in parallel. The national heritage of schismatic religion meant different rules applied in different places. From the mid- to late-Nineteenth Century, hardening of the Union and the closing of the frontier brought religious, moral, and political consolidation. American experiments entered their twilight, and The American Experiment was celebrated, integrally, which was no experiment at all, but only progress. + +‘Never change the rules’ is an example of a good meta-rule. What, then, exemplifies a bad one? ‘We should all be playing the same game’ is probably the very worst. At least, nothing more sinister can easily be conceived. + +We don’t like the same games. More particularly, we don’t all like the kind of domination game that requires everyone to play the same game, even if some like it a lot. The ‘game industry’ has an abundance of practical evidence on ludic preference diversity, far exceeding what is required to make the basic point. We want to play different games is the basic point. Despite its overwhelming obviousness, getting it installed as a default is surprisingly difficult. In part, this is Social Domination game-play at work. + +There are people who dislike chess. There are many more who don’t like it enough to play it continuously, and exclusively. Chess, nevertheless, is a well-constructed game. No one is disgraced by their dedication to it. + +Social Domination is a contender for the worst-constructed game in history. “Let’s keep changing the rules until everybody likes it,” it suggests tacitly. It simultaneously makes other suggestions which directly contradict this, but never to the point of ensuring its retraction. As if this were not already bad enough, it also mandates universal cheating. Its rules are so numerous, unstable, and poorly-formulated that they are both theoretically and practically unintelligible. The latitude with which rule-violations are to be avoided or penalized has become a strategic consideration. Players in weak positions have to scrupulously avoid gross rule-violations and are increasingly terrorized by trivial, absurd, and informal norms. Players in strong positions get to ignore any rules they don’t like. + +The best Social Domination players get to decide whether to permit opt outs from Social Domination. The incentive effects here are entirely predictable. However much you hate the game, you have to win it to escape. Those who like it are far more likely to do well at it. On the rare occasions when those who don’t like it do well, they suddenly find they like it more than they had thought, or have invested too much in it to quit. To escape it means fighting it, which means playing it, which means investing in it. Getting out involves putting people into a position from which they can get you out, and that position turns out to be a lot more comfortable than either getting out, or letting anyone else out. These dynamics are clear to everyone. + +As it all becomes ever more obvious, cynicism explodes. No one is any longer really fooled by the thinly-stretched, saccharine, hysterical idealism. It’s all power and who-whom, as the practitioners of Cultural Revolution are the first to admit. “We’re fucking you, and we get to call it good, because we’re winning, and you’re not.” That’s the whole of it. For anyone who thinks Social Domination is a great game to play, it makes more sense than it ever has. There are many such people. They’re not going away. + +“Is it time yet?” + +“It’s a bit later actually.” + +“It’s a bit later than now? Or now’s a bit later than it?” + +It’s time to war-game getting the hell out, and away from them. The technological platforms for it are almost in place. Begin to use them, and they’ll arrive faster. It’s all been set up in a way that can’t be stopped. The games industry is the template. + +Any exit ramp that looks serious is fake. Social Domination manages serious threats easily, making them actually non-serious. Such ‘challenges’ fall under its rules, dialectically, and merely make it bigger. There’s no way to seriously oppose it without playing into it. + +Any real exit has to be seriously non-serious. Game it out. Play another, different game on the side, shifting everything steadily to the side. Migrate intelligence-capital onto a million ludic frontiers, where exit hatches. No one will take it seriously until it’s too late. + +It’s getting ever easier to try things out inside games. Any kind of plotting that doesn’t take this route will soon seem obsolete. + +The means of simulation do not need to be seized, but they do need to be proliferated. Other frontiers will open, but none so soon. diff --git a/python3/transcript.txt b/python3/transcript.txt new file mode 100644 index 0000000..9807ebe --- /dev/null +++ b/python3/transcript.txt @@ -0,0 +1,286 @@ +# Nested Timers: A Communication Tool for Problem-Solving Work + +## The Core Idea + +I am building a time tracking tool that captures what problems you work on and when. The key features: + +- Track PROBLEMS you are solving, not tasks you are doing +- Nested structure: sub-problems are children of parent problems +- Visual span editor: drag boundaries to adjust times, like editing video clips +- Share visualizations: send a link or screenshot, no explanation needed + +## The Communication Problem + +When you work on something and it takes longer than expected, people ask: "Where did the time go?" + +Right now, you have to: +1. Reconstruct from memory what happened +2. Choose the right level of detail +3. Write it in a way that sounds professional, not defensive +4. Hope you included enough information that they do not ask follow-ups + +This takes 15-20 minutes every time, and the result depends on your memory and writing skill. + +## The Solution: Data Instead of Narrative + +With a span visualization, you just share a link. + +The recipient sees: +- Timeline of when you worked on what +- Nested tree showing problem relationships +- Visual proportions showing where time went +- Clickable spans with your notes and descriptions + +They can explore at their own pace. Common questions are preemptively answered: +- "Which part took longest?" → They see it in the graph +- "What were the sub-problems?" → They see the tree structure +- "Did you try X?" → They read your notes on that span + +## Why Problems, Not Tasks + +A task is: "Configure email service" +A problem is: "Email service returns 500 errors when sending password resets" + +Problems capture what you are trying to achieve or fix. Tasks are just actions. + +When you track problems: +- The description explains why you are working on something +- Sub-problems show complexity that was not obvious upfront +- The tree structure shows your decision-making process + +When someone looks at your timeline and sees you spent 4 hours on "Email service returns 500 errors" with 8 nested sub-problems, they understand the complexity. If they just see "Configure email service: 4 hours" it looks slow. + +## The Shape Tells the Story + +Different work has different shapes: + +**Straight-through work:** +``` +Problem A + └─ 3 hours +``` +Simple, focused, no complications. + +**Deep investigation:** +``` +Problem A + └─ Sub-problem B + └─ Sub-problem C + └─ Sub-problem D +``` +Had to solve 3 layers of dependencies first. + +**Interrupted work:** +``` +Problem A (2 hours) +[gap] +Problem B (1 hour) +[gap] +Problem A (2 hours, resumed) +``` +Got blocked, switched to something else, came back. + +**Scope creep:** +``` +Problem A (stopped) + Problem X (new top-level, 4 hours) +``` +Abandoned original goal, worked on something else entirely. + +Each shape tells a different story. With visualization, the shape IS the communication. You do not have to translate it into words. + +## Communication Scenarios + +### Open Source Maintenance + +Put a link on your profile. Users can see: +- You work on this 3-5 hours per week +- You are currently working on Issue #23 +- You worked on Issue #19 last week + +No status updates needed. The timeline shows you are making progress. + +### Technical Disagreements + +Colleague says: "Adding that feature should take 2 hours" +You think: "No, there is hidden complexity" + +You do the work. You track it. You send them the visualization. + +They see: 10 hours total, 15 sub-problems, each solved in 20-40 minutes (reasonable pace), just way more sub-problems than they predicted. + +They update their model. No argument about competence. Just: here is what actually happened. + +### Library Feedback + +You spend 45 minutes trying to use someone's library. The docs are unclear. + +Instead of: "Your docs are confusing" (gets ignored) +You send: Timeline showing 30 minutes searching docs, annotations of which pages you looked at, where you finally found the answer + +Concrete, actionable feedback. + +### Manager 1-on-1s: Mentoring on Decisions + +You do NOT use this to send status reports. You use it strategically in 1-on-1s to get mentoring on decision-making. + +Example: You show them your week. You point to a specific shape and say: + +"See this? I got pulled into working on Ticket #45 even though I was supposed to focus on the migration. I want to practice saying no to interrupts. Could you help me role-play: next time someone asks me to work on something urgent, how do I push back?" + +Or: "Here are two graphs. Two months ago, I was context-switching constantly. Here's last week - way fewer switches. Whatever I'm doing is working." + +This lets you: +- **Point to specific examples** of behavior you want to change +- **Show progress over time** ("I'm self-improving, here's the data") +- **Get concrete advice** on specific decisions, not vague principles +- **Practice skills** you struggle with (saying no, avoiding scope creep) + +You control when to show it. Maybe you walk them through it on your laptop. Maybe you show a screenshot. But you're strategic - you pick the moments where having a visual prepared helps make your point. + +### The "Went Overtime" Conversation + +You're not happy with this week. You got sucked into unexpected complexity. You show them the graph. + +"Look at all these sub-problems. I thought this would be 4 hours. It took 12. I'm not making excuses - I want to get better at estimating. Can you walk me through: what would you have done differently when you hit sub-problem #3?" + +The graph isn't to solve anything. It's a talking point. It shows: you've thought about this, you've organized the problems, you want to improve. + +### Why Problem Descriptions Matter Here + +Because you've already done the work to frame things as problems (not just "worked on ticket #45"), you have descriptions ready: + +- "Users can't reset passwords" (not "config email service") +- "JSON decoding takes 500ms" (not "performance optimization") + +When you're in the 1-on-1 trying to explain what's important to work on, you're not translating on the spot from "what I did" to "what problem I was solving." You already thought about this during the week. + +The work you put into describing problems pays off: the information is cached, ready to use when making a point. + +### Understanding Your Own Patterns + +After 2 weeks, you look at your timeline. You notice: +- 40% of time goes to dependency issues +- You always underestimate database work +- You start 5 new problems per day but finish 0 + +These patterns were invisible without measurement. Now you can make better decisions. + +## The Never Write Again Vision + +Ideal outcome: you never write status updates or explanations again. + +Someone asks what you did? Send link. +Why it took long? Send link. +What blocked you? Send link. +Is project maintained? Link on profile. + +The visualization is universal. Works for any time span (1 hour to 1 month), any complexity, any audience. + +You just track as you work. Visualization generates automatically. Anyone who wants to know looks at it. + +## Why Visual Editing Matters + +You cannot track perfectly in real-time. You will: +- Forget to start timers +- Forget to stop timers +- Realize later you were working on a sub-problem + +Visual editing lets you fix this quickly: +- Drag span boundaries to adjust times +- Nest spans to show problem relationships +- Split spans when you realize you switched problems +- Update descriptions as you understand better + +Like editing video clips in Premiere. Fast because you have immediate visual feedback. + +At end of day: 20-30 minutes to review and clean up the timeline. Much easier than reconstructing from scratch. + +## What Gets Tracked + +You create a span when you start working on a new problem. This happens maybe 20-30 times per day as you switch between problems and encounter sub-problems. + +You do NOT track individual steps like: +- Read documentation +- Write code +- Run tests +- Fix typo + +Those are actions within solving a problem. Too granular. + +You track problem boundaries: +- "Document decoding takes >500ms for 10% of requests" (start timer) +- Encounter sub-problem: "How do I measure JSON.parse() time vs class constructor time?" (create nested timer) +- Encounter another sub-problem: "Can I reproduce the slow JSON parsing in a standalone script?" (another nested timer) +- Sibling sub-problem: "Can I reproduce locally? Need script to make HTTP requests and measure timing" (another nested timer) +- Different branch: "What binary encodings are available? Need to research alternatives" (sibling problem) +- Another branch: "How do I load binary data from different sources to test different encoders locally?" (sibling problem) + +## The Data Is Queryable + +Because it is structured data, you can: +- Filter to show only work on Issue #37 +- Aggregate time by problem type +- Find all problems you worked on for >2 hours but abandoned +- See all times you got blocked on dependencies +- Generate different visualizations for different audiences + +Text notes cannot do this. You have to read everything and manually extract patterns. + +## How It Compares to Alternatives + +**vs. Text file / journal:** +- Text: linear narrative, hard to see structure, cannot query +- This tool: tree structure, visual patterns, programmatically queryable + +**vs. Brief summaries when asked:** +- Summaries: 15-20 minutes to write, depends on memory, customized per audience +- This tool: 0 minutes, just share link, recipient controls detail level + +**vs. Toggl / RescueTime:** +- Those tools: flat list of tasks, no nested sub-problems +- This tool: tree structure showing problem relationships + +**vs. GitHub activity / commits:** +- GitHub: only shows completed work +- This tool: shows work in progress, sub-problems encountered, time spent on blockers + +## Cost vs. Benefit + +**Daily cost:** +- 20-30 minutes tracking and editing throughout the day + +**Annual cost if using for 1 year:** +- Daily overhead: 20-30 min × 250 days = 80-125 hours +- Building the tool: 100-200 hours +- Total: 180-325 hours + +**Benefits:** +- Never write status updates again (save ~15 min/week) +- Better decisions because you see patterns +- Better communication with managers, colleagues, users +- Credibility in open source (people can verify you are working) +- Data to improve your process + +Hard to quantify the benefits precisely. But if you frequently need to explain where time went, and you struggle with verbal/written communication, the tool pays for itself. + +## Who This Is For + +This tool is for people who: +- Cannot accurately remember what they did hour-by-hour +- Frequently need to explain where time went (manager asks, colleagues ask, users ask) +- Work on multiple projects or explore new domains +- Struggle with writing clear explanations +- Think visually (graphs easier to process than prose) +- Want data to improve decision-making + +If you are already good at remembering, explaining, and making decisions, you do not need this. + +## Next Steps + +I am building this tool because I have these problems. I will document the development process and share what I learn. + +If you have these problems too, follow along: [project link] + +For answers to common objections, see: [FAQ document](./deep-work-timers-faq.md) +