u/Independent_Plum_489

I started tracking my online purchases in a spreadsheet back in February. Not to budget, just to see how often I was actually using a coupon code before hitting "place order." The answer was almost never. I'd Google something, paste in an expired code, get annoyed, and just pay full price. Over roughly 40 orders across three months I did that on nearly every single one. Embarrassing.

Somewhere in March I installed coupert after someone on here mentioned it. It just tries codes for you, which is honestly the only reason I stuck with it, because I was never going to sit there pasting codes myself. Worked on maybe a third of my orders. Sometimes it saved a couple bucks, sometimes closer to $8 or $12. It also tracked cashback on some purchases that eventually paid out as actual money, which I genuinely did not expect.

Between that and forcing myself to wait a full day before any impulse purchase, I ended up at just over $94 saved on stuff I was already buying. That covers a month of pet food for me. Turns out my problem was never that I bought expensive things. I was just too lazy to spend 10 seconds not paying full price.

I track my side project spending because otherwise i pretend it doesnt exist. last month: 312 dollars. mostly gpt 5.4 through openrouter, some claude opus for the messy stuff.

Kimi k2.6 dropped recently at around 2 dollars per million output tokens. gpt 5.4 is about 15. i did the math and switched my coding agent on saturday.

This month im looking at about 44 dollars. not a typo. 86 percent drop.

Switched over in verdent, picked k2.6 from the model picker. First task was a refactor id been avoiding. output was solid, actually caught a race condition in my auth flow that i had missed.

Ran my standard test prompt, full stack feature with auth and db. cost was 0.06 versus 0.28 before. quality was comparable, maybe slightly more verbose but nothing i couldnt clean up.

The weird part is the psychology. at 312 a month i was rationing prompts, thinking is this task worth the tokens. at 44 i just run it. i try stupid ideas i would have skipped. the tool stops being a budget item and starts being fun again.

For side projects this is a no brainer. the money i save goes straight into actual infrastructure instead of burning it on tokens.

I've been a Pro subscriber since early 2024 and Perplexity is genuinely my go to tool for 90% of research tasks. Summarizing papers, comparing products, pulling together news on a topic. It's excellent at all of that.

But I also do a lot of crypto research, and I've hit a wall that I think is structural, not something a prompt tweak can fix. I want to walk through a specific example because I think it illustrates a broader limitation of general purpose AI tools in domains that depend on real time, specialized data.

The task: I was researching a relatively new token that had just launched on a major DEX. I wanted to understand three things: the on chain activity (whale accumulation, smart money flows, liquidity depth), what crypto Twitter thought about it (KOL sentiment, narrative momentum), and the tokenomics (vesting schedule, unlock timeline, team allocation).

What Perplexity gave me: It pulled a CoinGecko page that was about 8 hours stale on price. It cited a Medium article from the project's team that was two months old and described a tokenomics structure that had already been revised via governance vote. For social sentiment, it summarized a few tweets but couldn't distinguish between genuine KOL takes and random engagement farming accounts. When I asked specifically about whale wallet movements in the last 24 hours, it told me it couldn't access on chain data directly and suggested I check Etherscan manually. The Deep Research mode did better on structure but still lacked any real time chain data.

I don't blame Perplexity for this. It's a general purpose research engine built on web search and LLM synthesis. It doesn't have a direct pipeline to blockchain nodes, it doesn't index crypto social accounts at scale, and it doesn't maintain real time derivatives or liquidity data. That's not a bug, it's just not what it was built for.

What I got from a specialized tool: I ended up trying Surf, which is specifically built for crypto research. Same questions, completely different output. It pulled the actual on chain flows for that token in the last 24 hours, flagged three wallets with smart money labels that had accumulated significant positions, showed me the current liquidity distribution across pools, and gave me a sentiment breakdown based on tracking over 100K crypto KOLs with parsed tweet data. The tokenomics section reflected the updated vesting schedule, not the outdated Medium post. It even surfaced the funding rate and open interest data from derivatives markets, which was relevant because the token had just gotten a perps listing.

The whole thing took maybe two minutes versus the 45 minutes I'd spent trying to piece it together across Perplexity, Etherscan, CoinGecko, and Twitter search.

Why I think this matters for Perplexity's roadmap: This isn't just a crypto problem. It's a vertical data problem. Any domain where the information changes by the minute, where the authoritative data lives in specialized databases rather than web pages, and where hallucination risk is high because the LLM's training data is stale... that's where Perplexity's current architecture struggles. Crypto just happens to be one of the most extreme examples because prices move 24/7, on chain data requires node access, and the social layer is incredibly noisy.

I'd love to see Perplexity eventually build integrations or partnerships that give it access to real time structured data in verticals like this. The research UX is already best in class. If it could pull live on chain data and verified social sentiment the way it pulls web sources today, it would be unstoppable.

For now though, I use both. Perplexity for general research and crypto macro narratives, and a specialized tool when I need to go deep on a specific token or protocol with real time data. It's not ideal, but it works.

I work in audio DSP and was recently diagnosed with mild sloping hearing loss, so naturally I went down a rabbit hole trying to understand how OTC hearing aids actually decide what to amplify and by how much. I see a lot of posts here from people asking whether their audiogram falls in OTC territory, and I think understanding the fitting algorithm side of things can help frame that question better.

Most of the cheap OTC amplifiers I looked at first are basically doing one of two things: flat gain (everything gets louder by the same amount) or a simple EQ curve where you drag a few frequency sliders around manually. Both of these are fundamentally guessing. You might boost 2kHz because dialogue feels muddy, but you have no principled basis for how much gain that frequency actually needs relative to your specific hearing thresholds.

Then I started reading about NAL-NL2, which is a prescriptive fitting formula developed by the National Acoustic Laboratories in Australia. Audiologists have been using it for years in clinical fittings. What it actually does is take your audiogram (your measured thresholds at each frequency) and calculate a specific gain prescription per frequency band. It accounts for things like the loudness growth function at different hearing levels, so someone with a 35dB loss at 4kHz gets a different gain prescription than someone with a 25dB loss at the same frequency, and crucially, the relationship is not linear. NAL-NL2 optimizes for speech intelligibility while keeping overall loudness comfortable, which is a much harder problem than just "make it louder."

What surprised me is that some OTC devices are now implementing NAL-NL2 directly in their companion apps. I found this on an ELEHEAR device where you input your audiogram and the software generates a frequency specific gain curve based on the NAL-NL2 prescription. That means the DSP is applying different amounts of amplification across the frequency spectrum according to a clinically validated model, not just a user's best guess with a graphic EQ.

To be clear about what this does and does not mean: having NAL-NL2 in an OTC device does not make it equivalent to a professional fitting. A clinical fitting involves real ear measurement where the audiologist verifies the actual sound pressure at your eardrum, accounting for your unique ear canal resonance. The OTC implementation is working from your audiogram alone, so it is an approximation. But it is a dramatically better approximation than flat amplification or manual EQ, because the underlying model was built on decades of loudness perception research across thousands of patients.

For the people asking "is my audiogram in OTC range," I think the more useful question is whether the device you are considering actually does anything intelligent with your audiogram data. A device that takes your thresholds and runs them through NAL-NL2 to generate a proper gain prescription is doing something fundamentally different from one that just lets you turn up the volume. That said, OTC devices are designed for mild to moderate hearing loss. If your thresholds are pushing into severe territory, a prescriptive formula in an OTC app is not a substitute for seeing an audiologist who can do verification and work with more powerful hardware.