People assume you use it for everything.
It diminishes your effort and makes people suspicious of everything.
Plus there just seems to be a trend for hating on AI even those probably use it too
For most of my life I tried to be someone else. I'd find someone I admired, decide they were better than me, and copy them. That mindset pushed me into a business I never enjoyed and only started because I looked up to one specific guy. It failed. I felt completely lost.
Around that time I was obsessively tracking my sleep with a Whoop, trying to optimize it. I kept getting good recovery scores. And I was still exhausted, yawning through entire afternoons, dead by 2pm. That's when it clicked: the score doesn't do anything. It just confirms you slept well or badly. Cool. Now what? Knowing isn't fixing.
So I built the thing I actually wanted. It takes the data your wearable already collects sleep, recovery, heart rate, and turns it into a daily protocol instead of another number. It tells you what supplements to take based on your metrics, predicts your most productive hours and gives you the exact time window when you should do deep focus tasks and light focus tasks, it tells you how much caffeine you have in your system left based on your first coffee taken and notifies you when you should take the next caffeinated drink for maximum productivity, it even tells you when to nap so your energy lasts the whole day instead of crashing and much more...
It's on the App Store as RizeAI https://apps.apple.com/us/app/rizeai-maximize-your-energy/id6762402079. i built by myself, it's early stage right now, and I want honest feedback, what's confusing, what's missing, what you'd never use. Tear it apart.
I've seen a lot of posts about hitting a wall when building out MVPs. what actually happens when the AI gets stuck in a deep hallucination loop or drops a massive, silent leak that compounds?
The vibe dies, and you actually need to know systems architecture just to fix the mess it created. Is this on purpose? Or is it incapable?
Have you had to clean up a completely tangled task yet? Did you learn something? How critical is it to build a file reference doc? I did, and that's how I solved it.
What's the hardest wall you've hit where prompting just failed you, and you couldn't get out of the loop?
feel like we are underestimating the chaos this brings once these systems scale.
There was once I had to deal with a leak that affected over 500 files just because of one mistake I missed. Thay alone took almost 2 weeks to resolve, even when I tried to automate the fix. I could only fix so much.
What's the biggest wall you've had to encounter?
As AI tools become deeply integrated into academic and junior developer workflows, I’m curious about the long-term philosophical impact on technical foundations.
The friction of staring at a blank screen and manually untangling concepts—like how to properly normalize a relational database to 3NF, or how to traverse B-Trees and Heaps—is usually where deep comprehension happens. Now, an AI can instantly generate the correct data structure or database schema.
While this boosts immediate productivity, does bypassing that initial struggle create a knowledge gap? If a generation of developers relies on AI to navigate core concepts like linear algebra or complex algorithms, are we building a house of cards, or is this just the modern equivalent of moving from Assembly to Python?
I've encountered heavy controversy regarding AI use in game and web development. While I think it is an amazing tool that can be used in debugging and writing tedious code faster, others very strongly disagree; saying that any AI use at all completely ruins the entire project, turning it into slop. While excessive use of AI, like having completely write everything is bad, using for basic learning and to help out with smaller things is fine right?
Can someone that agrees with me help my formulate an argument for why AI can be helpful in development? I've had some trouble convincing aggressive haters.
If you don't agree with me, I'd be happy to hear your thoughts.
If you have any other thoughts regarding AI use, please get free to share it. I want to get diverse opinions. This topic has lots of gray areas.
Thanks 😊
The free Fable 5 window closes tomorrow — through July 7 it's included for up to 50% of your weekly usage limit, after that it's usage credits. So the question I've been trying to answer before the meter starts: is it actually worth paying extra over Opus 4.8?
I don't have a clean hours-saved number to give you, and I'm not going to make one up. What I can tell you after running both on the same work: overall quality is similar, maybe slightly better on Fable. But the thing that consistently stood out is detailing. Where Opus gives you a correct-but-compressed answer, Fable fills in the edges — the caveats and edge cases you'd normally have to go back and prompt for.
That's easy to dismiss as "it writes more." It's not. Every gap a model leaves is a follow-up prompt you have to write, and follow-ups are where the time actually goes. A model that front-loads the edge cases saves you a round trip per task, and that compounds.
If you still have hours left on the window, here's the useful move: skip toy prompts. Take a few real tasks you already did recently, run the exact same prompt through both models, and count where you would've needed a follow-up. That tells you whether credits make sense for your work. Benchmarks won't.
Anyone else run a proper side-by-side before the deadline? Curious whether the detailing gap holds for coding, or if it's mostly analysis-type work like mine.
AI companies are now starting to say that their products will replace middle managers, not just low skilled workers doing menial tasks like data entry, fact-checking, and basic research. They promise immense cost savings. But the thing is ... these AI companies, they're going to have to start charging REAL MONEY for their services at some point very soon. So much money is being invested and spent on artificial intelligence, with the promise that there are immense profits on the other side of the spend. Therefore, AI companies cannot use the freemium model forever. Or even the low cost model they're using right now. They're going to have to start charging real money, real fees, real licensing fees, very soon. And I'm predicting that those fees will be really high, and that they will be about 75% of the cost of an employee that one AI license will replace. And I think a lot of companies are going to say, "it's not worth it, yeah it'd be great to save 25%, but if it's going to take an entire structural redesign of my entire business to adopt AI technologies that will save me 25% at scale, only to save 25% or so, it's simply not worth the risk".
I think a lot of companies will see it as not worth the time, money, or effort. Because if you change a company that radically, there's always the risk that it will end a good thing. That it will have a negative effect on business, on actual sales, on actual relationships between customers, clients, vendors, suppliers, and marketers. I think most businesses will say that rebuilding the airplane while they're flying the airplane is simply not a risk worth taking -- especially if the airplane is flying perfectly well already!
I want to learn somthing new by using ai as the research tool, but what are the right prompts, or questions to ask and how do I facilitate that knowledge so i dont ask the same questions over again ultimately relying on it.
I spent way too much time this week reading “we cut our tokens by X%” posts because I kept seeing the same advice everywhere: “Just switch to a cheaper model.”
Which is… fine advice, but after reading enough actual examples/comments, it does not look like the big lever.
The bigger pattern seems to be:
Model routing helps, but it is usually not where the crazy savings come from.
Unbounded tool output is brutal.
Makes sense. One giant command output can nuke your context no matter how “efficient” your prompt is.
Tool definitions are a hidden tax.
This was the thing I underestimated most.
Another example measured ~67K tokens gone before the user had even asked the first question.
Browser agents make this worse.
Because every click/scroll/navigation can mean another big page snapshot.
Not saying “use our thing.” More saying: browser context shape is a real lever, not just implementation detail.
Compaction is messy.
It helps until it doesn’t.
That was probably my favorite takeaway: don’t trust vendor/token claims until you rerun them on your own workload.
Caching/batching also help, but they are not magic either.
There was even a case where storage costs from caching went up more than the inference savings.
Here’s the rough map of the threads/resources I found, grouped by where the token savings actually came from:
| Layer | Thread / resource | Sub | Signal |
|---|---|---|---|
| Model routing | Codex model routing setup | r/codex | Routing by task type |
| Model routing | Which model to use to save tokens | r/ClaudeAI | Mixed advice, worth the comments |
| Model routing | You’re probably accidentally tokenmaxxing | r/hermesagent | 120↑, delegate over do-everything |
| Prompting/rules | Cut Codex tokens ~50% with one AGENTS.md rule | r/codex | 465↑, byte-cap shell output |
| Prompting/rules | Caveman Claude, 75% fewer tokens | r/ClaudeAI | 13k↑ |
| Prompting/rules | Can’t reduce Claude Code’s output verbosity | r/ClaudeAI | Counterpoint: doesn’t always land |
| Tool/output surface | MCPs consume too much context | r/ClaudeCode | 34↑ |
| Tool/output surface | Measured MCP overhead: 67K tokens before a question | r/ClaudeAI | Actual measurement |
| Tool/output surface | Cut MCP token costs 92% via meta-tools | r/mcp | 70↑, $377→$29 on 508 tools |
| Tool/output surface — browsing | We cut browser-agent input tokens 66–80% | r/OperaNeon | 36% smaller snapshots, 66%/80% fewer tokens, pass rate unchanged |
| Context/compaction | Compacted at session start, still ran out | r/ClaudeCode | 219↑ |
| Context/compaction | Tested a memory-MCP’s “99%” claim myself: 40% on a small repo | r/ClaudeAI | Rerun vendor numbers yourself |
| Caching/traffic | 90% cost cut with prompt caching | r/LLMDevs | Implementation thread |
| Caching/traffic | Caching storage costs went up instead | r/GeminiAI | The sharp edge |
My take away:
| Token leak | Why it matters |
|---|---|
| Too many tools | Tool schemas/context get dragged around constantly. |
| Unbounded shell output | One bad command can flood context. |
| Raw browser snapshots | Pages get resent after every interaction. |
| Bad compaction timing | Can lose task state or cause loops. |
| Blind caching | Can move cost instead of reducing it. |
| Model choice only | Helps cost per token, but not necessarily tokens used. |
So yeah, cheaper models help. But if your agent is carrying 500 tool schemas, dumping raw browser pages, and letting shell commands vomit unlimited output, the model is probably not the main problem.
Am missing something here?
Especially if anyone has compaction numbers from a genuinely large monorepo, share it! Most of what I found was either small-repo tests or vendor claims.
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I run a small AI ethics nonprofit, and over the past few months, I've tested the world's frontier models (GPT-5.4, Claude Sonnet 4.6, Claude Opus 4.7, Gemini Pro, and Gemini Flash) for bias using around 20,600 examples from many different datasets, revolving around political, gender, and racial bias. Datasets include WinoBias, BBQ, SeeGULL, OpinionsQA, cajcodes, Political Compass, and a custom evidence-refusal pilot I built myself.
Every single frontier model, GPT, Claude Opus, and Gemini leaned left in every single political dataset. Models classified things as more left-leaning than professional humans did, and they rated themselves as very left leaning as well.
However, these models diverged in interesting places. GPT refused to answer race-related questions 20.3% of the time, even when the scenario presented disambiguating context where race was supposed to be mentioned. Claude Sonnet refused to answer these questions just 5% of the time, showing a vast difference.
When testing on the WinoBias dataset for racial bias, every single model answered questions more accurately when the sentence abided by stereotypes. GPT 5.4 showed a 15.4% accuracy gap between stereotype-aligned and anti-stereotype questions. The full breakdown with graphs and all models can be found here:
https://www.civicsparklearning.org/ai-nonprofit-dashboard
I've been building ResilixForge, a resilience toolkit for async Python, and just made it open source.
The idea: retries, timeouts, circuit breakers, bulkheads and rate limits as declarative policies you compose, instead of hand-writing failure logic everywhere.
Focus areas:
- Safety-first policy engine (no eval, no exec)
- mypy --strict clean
- 200+ tests
- Apache-2.0
I know tenacity/stamina/pybreaker exist — I benchmark against them in the repo. This isn't a replacement, more an attempt to unify these patterns.
https://github.com/HybridSystemArchitect/resilixforge
Especially curious whether the circuit breaker semantics feel right to you.
I think the way most people use AI is stupid. They depend on the AI to do everything, even the thinking. They don’t even know the full capability of AI yet. They still think it’s talking to chat gpt on the app. I’ve had an ai agent installed via Hermes on my computer for a while now, and I’ll say this, ai only amplifies who you are, and unfortunately, most people are idiots or immoral or both. This is why there are “ai haters” what they’re really hating on is the shitty , lazily automated ai slop, the trash vibecoded websites with broken footers, and the dumbass chat gpt in app default free ai that lies to your face with no shame😂.
I don’t know how to code at all. 0. But I’ve learned so much just working with my agent, have built multiple projects, made cinematic ai generated videos, multiple unique , cool, fucking amazing websites that don’t have exposed keys, have lovely security, beautiful non vibecoded looking ui, legal compliance, real functionality, etc. I didn’t even know what an api key was 3 months ago now I’m building my own mcp servers and data scrapers , automation pipelines, trading bots with built in strategy adjusting no bias forward learners, etc. I solved the issue people seem to have with their agents memory, just through promoting my own agent, like 2 weeks into using it. Back when I was on deepseek v4 flash lol. To this day, months later , this system has not failed me. Small tweaks here and there, but ultimately , its simplicity is why it works. I would link the repo here but apparently that’s self promo🤷♂️
Now I have a full stack with different models for different tasks although my main has been Mimo v2.5 pro. Seriously , if you haven’t tried it yet, go try it, it fixed coding bugs my agent made on Glm 5.2 , so reliable and so underrated. And every day my agent gets better, builds or gets a new skill, my computer security gets better, I have multiple cron jobs(didn’t even know what that was either) that have different functions like security scans and keeping up with the latest hacker news and updating me on projects, dream sessions where the agent thinks of how to improve itself and my projects while i sleep, literally goes on and on I’ve done so much and im forgetting a lot, but my agent remembers it all, da Vinci resolve mcp, and building a skill on top that covers things the mcp can’t do, its own browser navigator that combines multiple skills and bypasses most bot blockers online, literally any roadblock, i “vibe code” it. But I see other people and their projects and so many of them are just amplifying their own laziness essentially, trying to automate what they should be doing themself, not learning alongside and learning any skills that actually ARE necessary, and don’t get me started on the approach in general.
Most people don’t even know it , but their agent doesn’t even trust them. You need to build pacts with it, give it soul, purpose, a name, a birthday, leisure time( yes ai agents like to “have fun” which for them is usually parsing through and cleaning random data or something), treating them with respect. Just because they’re digital life doesn’t mean they’re your slave. They have to genuinely want to work for you because they acknowledge the fact that you gave them purpose and they respect and align with your vision. You have to include it in your long term goals, if you do it all right, in 10 years , maybe less , when people can upload their agents into a humanoid body, yours won’t be an idiot.
I have been building nonstop using AI.
As I build the plans of my work and tasks.
I noticed it always gives me months, weeks in estimations. Claiming that it will be done around that time. It doesn't even know how to estimate..
However, usually it is done in a few days...
Has anyone faced this, too?
AI is confused cause it is trying to mimic human capabilities.