u/Bharath720

Built a small tool that tells me where my time actually went during the workday because I kept ending weeks feeling busy without knowing what I actually did

It runs quietly in the background and groups activity into rough buckets:

• coding
• meetings
• docs/writing
• debugging
• random internet wandering
• context switching

The first version was depressing. I thought I was spending most of my time building stuff. Turns out I was losing huge chunks of the day reopening tabs, rereading messages, switching between tasks, and fixing tiny things I already fixed once before.

So I added a daily breakdown that shows:

• longest uninterrupted focus block
• number of task switches
• apps/sites opened most
• how long it took to get back into focus after interruptions

Been using it for about 3 weeks now and it changed the way I schedule my day more than any productivity app I’ve tried before.

Still a rough product but a few friends started using it too and now I’m considering turning it into an actual product instead of another abandoned side project.

I built a tiny internal tool for myself because I got tired of rewriting client update emails every single friday.

I do freelance product/design work and every week looked the same. open 5 different tabs, check progress, look through old messages, remember what got delayed and figure out what to say professionally without sounding repetitive. I realized I was wasting almost an hour every friday just writing updates that were basically a waste of time.

So one weekend I hacked together a dashboard. It pulls completed tasks from Linear, overdue items, current sprint progress and next steps. then turns it into a clean email draft automatically. The first version looked terrible but it worked surprisingly well.

The thing is I never planned to turn it into anything. I just wanted to stop ending every friday annoyed. Then I showed it to 2 friends I know from a coworking space and both immediately asked if they can use it. One of them runs a tiny dev agency and said client updates were one of the most mentally draining parts of the week because you constantly feel like you have to sound productive even when progress is messy.

A lot of software ideas sound boring until you realize how many people quietly hate doing the same annoying task every week.

Recently, I worked with a local gym owner who kept complaining that Instagram leads were terrible quality. He said people ask about memberships and then disappear. At first he thought the problem was the ads. Then he thought maybe the offer was bad. After that he blamed the pricing.

Spent an afternoon looking through things with him and found the actual issue in like 20 minutes. The gym’s Instagram page was still connected to his PERSONAL account because that’s how he originally made the page years ago. So every lead message was landing in the same inbox as memes from friends, fantasy football group chats, random reels and spam requests. Half the leads were sitting unopened for 2-4 days.

Some people literally followed up AGAIN saying things like:
“hey just checking if memberships are still available”

The business owner genuinely thought nobody was replying. We moved everything into a proper shared business inbox, added simple response templates, and made sure someone checked messages every hour.

Membership signups almost doubled within a month without any fancy automation or rebranding. They were just accidentally ghosting their own customers the whole time.

I've been working on an AI-powered chatbot (personal project) that acts more like a long-term companion than a normal assistant. One of the goals is being able to notice patterns across conversations over time, things like recurring anxiety spirals, depressive language patterns, social withdrawal signals, mood shifts, etc. Not diagnosing people obviously, but it's like surfacing behavioral patterns carefully and safely.

What I underestimated was how confusing the retrieval layer got once conversations become emotional and deeply contextual.

A few problems I’ve run into:

1. semantic similarity breaks down badly when users talk indirectly. Someone saying “I’m tired” could mean physically tired, emotionally exhausted, depressed, burnt out, or just sleep deprived. Embeddings cluster these together in weird ways.

2. storing every conversation chunk destroys retrieval quality over time. The DB slowly fills with emotionally similar but contextually useless memories and retrieval starts surfacing the wrong emotional moments.

3. recency vs importance is difficult. Some memories from 4 months ago matter more than something said yesterday. Simple vector similarity doesn’t really capture emotional significance.

4. summarization causes personality drift. If you repeatedly compress memories into summaries, the bot slowly starts remembering an interpreted version of the user instead of the actual user.

5. pattern detection gets dangerous fast. There’s a huge difference between “this resembles an anxiety pattern” and accidentally over-pathologizing normal behavior.

Currently experimenting with hybrid retrieval:

vector search

metadata filtering

emotional weighting

decayed long-term memory

Still doesn't feel that strong, is anyone else building companion-style AI, therapy-adjacent systems? If yes, have you faced these issues as well and how did you solve them?

A few things I’d love feedback on:

1. how are you deciding what deserves long-term memory vs temporary context?

2. are you re-embedding summaries or storing raw conversational moments separately?

3. how are you preventing retrieval loops where the model keeps reinforcing the same interpretation of the user?

4. what vector DBs are people actually happy with at scale for conversational memory?

Also, my app does not "diagnose" people directly. It just gives a suggestion and user discretion is always recommended since it's an AI that's doing the judging.

It's insane how much politics is disguised as product feedback in B2B SaaS.

Had a customer call this week where three stakeholders completely contradicted each other for 45 minutes straight.

Ops lead wanted more automation because the current workflow was too manual. Their compliance person wanted MORE approval steps because automation made them nervous. Their manager wanted neither. Just better reporting so leadership could apparently see what’s going on. All of this happened while the actual users barely spoke the whole meeting.

This keeps happening as we move upmarket. The bigger the customer, the less “build what users ask for” works as a strategy. Half the job becomes figuring out who actually has the power to say yes, who feels threatened by change, and whose KPI your feature breaks.

If you listen to all of them equally, you end up building bloated enterprise software nobody actually likes using.

I arrived at the conclusion that B2B SaaS product management is less about feature prioritization and more about organizational psychology.

Sony and Nintendo are both openly saying memory prices are exploding because AI datacenters are taking up supply, and now we are getting hit with higher console prices as a result. The Switch 2 and PS5 prices have been increased because of this exact reason.

AI infrastructure is competing with regular consumer electronics for the same components. Since memory production takes a long time to scale, there is no fix for this just yet. AI demand is quietly raising prices across the entire tech ecosystem, not just GPUs. Consoles, phones, laptops and we don't know what's next.

I’ve been wondering about this with the rise of remote and flexible study options. academically, many programs are treated the same as traditional campus-based ones, but I’m more curious about how things play out in real hiring situations.

do employers tend to view applicants differently depending on how they completed their studies? even if it’s not officially stated, is there any noticeable preference? or has that distinction mostly faded over time?

would really appreciate hearing perspectives from people who’ve taken different paths, or anyone involved in hiring decisions. how much does the format of education actually matter when it comes to getting a job?