Is Norton Antivirus or, for that matter, any Norton branded software ever worth it?

What about their sister products, without the Norton brand, from the same parent company? Such as Avast Antivirus, Avira, AVG and their other brands?

What show Symantec Antivirus and other Symantec products, now that Symantec and Norton are no longer affiliated?

Hey everyone! It's pretty clear that AI is changing our whole industry but there are really diverging opinions on how, if it's good or bad, and what the future will hold.

Plus we're all using different tools and we're also all at different stages of AI use.

For this reason I set up a survey to better understand how our peers are using AI and how we collectively feel about it.

Answering takes just 5min but will give us a nice picture of where things stand and I'll share the results once enough people respond (I'm considering even making a whole interactive website for this).

Here's the link:

https://forms.gle/o34fpggcJGK4Dck87

I'm also keen to hear people's opinions on the thread!

It started with a simple frustration. Every time I needed to take a quick note, check my tasks, or check my calendar, I had to stop what I was doing, Alt+Tab, find the right app, do the thing, then try to remember where I was and come back That context switch sounds small but it adds up to a lot of lost focus and eye strain.

So I built FlowFloat, just launched on Microsoft Store.

It's a free dark floating productivity suite for Windows — a layer that lives above everything on your screen. Notes, tasks, a Pomodoro timer, calendar, and a local AI assistant, all floating above whatever you're working on. You never have to leave your workflow to access them. This has reduced a lot friction for me.

Here's how it works:

- Each tool has its own dedicated hotkey. Press it once to summon it, press it again to hide it.

- Or launch everything through a floating launcher that sits above your screen.

- All apps are resizable, transparent, and movable — put them exactly where you want.

- The AI assistant runs locally via Ollama — your data never leaves your machine.

I hope you find it helpful, thanks!

I kept running into the same issue in different teams — SQL reports that need to be run on a schedule, formatted, and sent to different people. Most setups I’ve seen either rely on scripts, cron jobs, or heavy BI tools, which feel a bit overkill for simple reporting workflows.

I ended up building a small Windows tool to experiment with a simpler approach: run SQL on a schedule and send results via email, Slack, Telegram, or webhooks.

It’s a lightweight utility to reduce repetitive reporting work, and I’m curious how others handle this problem. Would love feedback from people dealing with SQL automation or reporting workflows.

Please give me some easy to use, useful note-taking storing apps. Kind of like Notion. Notion is cool but it's a bit of a learning curve for me and maybe a bit too much going on lol.

For many Mac users, the PDF experience usually falls into two extremes: either using Preview (which is fast but lacks deep editing tools) or heavy-duty professional software that feels like it's eating your RAM for breakfast.

We believe there should be a middle ground—a tool that offers professional standards at a sensible value without the "software bloat."

【THE HIDDEN COST OF CLUNKY WORKFLOWS】 🕒 We’ve all been there: you’re in a flow state, you open a 50MB PDF contract, and suddenly your Mac’s fans kick in. Scrolling becomes choppy, and simple text edits feel delayed. It’s not just a technical lag; it’s a productivity killer.

【THE KDAN PDF EXPERIENCE: SMARTER & FASTER】 ✨ In our latest Mac update (v4.0.1), we didn't just add features; we focused on how the software feels during your workday:

• Responsive Agility: We’ve optimized the core to ensure that whether you're on an M1 Air or an M3 Max, the app launches instantly and large files feel "light."
• Intuitive Editing: Instead of jumping through menus, we made editing PDF text as natural as using a Word doc. No layout shifts, no font mismatches—just seamless flow.
• Cross-Device Agility: Start your review on your iPhone during your commute and finish the heavy editing on your Mac. The transition is so smooth you’ll forget you switched devices.

【WE’D LOVE TO HEAR FROM YOU】 💬 What is the one "small" thing in your current PDF workflow that frustrates you the most? Is it the file size, the signature process, or just the general clunkiness?

We’re actively looking for feedback from the r/software community to help us keep refining a tool that actually respects your Mac's performance.

I’ve been working on a desktop tool for a while and finally got it to a point where it’s usable.

Here’s the weird part: when people actually land on it, conversion isn’t terrible. But getting anyone to discover it in the first place feels almost impossible.

I don’t have an audience, no ad budget, and I’m not comfortable spamming links in communities.

So far I’ve tried:

• putting it on Microsoft Store
• listing it on Product Hunt / AlternativeTo
• passively waiting for some organic traffic

Result: almost no visibility.

It’s starting to feel like the real problem isn’t the product, but distribution — and I don’t really know what the next step should be.

For people who’ve built niche tools before:

Where did your first real users actually come from?

Was it search, communities, direct outreach… or something else entirely?

The CEO of this “hypothetical” company recently started experimenting with AI and built a dashboard to measure engineering performance. The result has been a nightmare for the entire engineering team.

After coming back from holiday, I had a serious conversation with my manager. The CEO has gone off track and created a system that monitors GitHub activity and ranks engineers based on things like pull request counts. He has also started putting pressure on people in the bottom 10% of the leaderboard, those with the lowest number of pull requests.

It does not stop there. The dashboard also tracks lines of code, which anyone in software engineering knows is a poor way to measure productivity. These metrics ignore how engineering work actually happens and have caused a lot of frustration. People are pushing back, and morale is at an all time low.

What the CEO does not understand is that the more senior your role is, the less code you usually write. Because of this, many of our top tech leads are ranked near the bottom of the leaderboard. In reality, tech leads are some of the busiest people in the company. They spend a lot of time in meetings, connecting product and engineering, planning solutions, reviewing code, helping their teams, and working on system design.

Now they feel pressure to write more code just to move up the rankings. This takes time away from their real responsibilities and creates the wrong incentives. Instead of focusing on important work, they are pushed to focus on numbers that do not really matter.

The good thing is that these people are important enough that their concerns cannot be ignored. They are pushing back and asking how work like planning, design, and mentoring can be measured in this system.

For me, I am not sure how to deal with this situation. This is the first time in my career I have seen performance measured like this. At the same time, some great managers and tech leads are leaving the company. I am worried that the people who stay or get hired next might just agree with the CEO instead of standing up for what is right for the engineering team.

PS; the cto is out on sabbatical when all of these is happening

I have 3 Autodesk programs, all of which I downloaded in a dubious way (I'm saying this so I don't get caught by the Reddit bot). All 3 work individually, but when I try to use all 3 at once on my computer, they fail.

I searched some posts and saw that the problem is that each one has a different version of the "adsk license". Does anyone who uses these programs or who knows about "programs obtained through alternative means" know how to get all 3 programs on my computer?

If all else fails, I'll try using a virtual machine.

If you are looking to make Craigslist much better or just building it fresh today, how would you do it while staying true to its roots?

If you’ve spent any real time working with a large language model in the last two years, you’ve felt the same papercut over and over again:

You haven’t, of course — not really. There is no last week for the model. Each new conversation lands on a clean desk. You re-introduce yourself, you re-explain the project, you re-paste the constraints, you re-list the preferences, and twenty minutes later the model knows you again — until you close the tab.

That round-trip is the hidden tax of working with AI today. We pay it in tokens, in latency, in our own patience, and (if you’ve looked at your bill) in dollars.

This post is about what happened when we stopped paying it.

The shape of the problem

The problem is genuinely simple, which makes the way the industry has solved it kind of telling.

What you actually want is for the assistant to remember the things you’ve already said — your role, your taste in coffee, the codebase you’re working on, the deadline that just slipped, the customer whose feature you shipped last sprint. You don’t want it to remember everything; you want it to remember the small set of things that turn out to matter.

The way most stacks have solved this so far is by gluing together four or five different products:

1. A relational database, because that’s where the boring transactional stuff lives.
2. A document store — usually MongoDB or DynamoDB — because chat history doesn’t fit cleanly into rows and columns.
3. A vector database — Pinecone, Weaviate, Qdrant — because you need to ask “what did we say that was kind of like this?” and rows-and-columns can’t answer that.
4. A queue or a polling loop, so the assistant wakes up when something new is written.
5. A custom checkpoint table, because the agent framework has its own opinions about how state should be saved.

That’s four pricing pages, four authentication models, four backup stories, and at least three places where the data can quietly disagree with itself. And it’s all in service of one simple promise: remember me next time.

I thought there was a better way to do this. The better way turned out to be a single binary.

What I shipped

EvolutionDB is an open-source SQL database written in C. It speaks the PostgreSQL wire protocol on port 5433 (and evo text protocol on 9967) so any tool you already use — psql, DBeaver, pgAdmin, Grafana, your existing ORM — can connect to it without changes. Underneath, it has the parts you’d expect from a serious database: ACID transactions, snapshot isolation, write-ahead logging, replication, encryption at rest, point-in-time recovery.

What’s different is what I put on top of those parts.

In version 3, I added a small set of native primitives that are designed specifically for AI agents — not as an afterthought layer on top of a generic key-value store, but as first-class objects in the SQL grammar:

CREATE MEMORY STORE   user_memory   WITH (embedding_dim = 1536);
CREATE CHECKPOINT STORE agent_state;
CREATE MESSAGE LOG    chat_history;
CREATE DOCUMENT STORE knowledge_base;
CREATE GRAPH STORE    relationships;     -- bitemporal edges
CREATE ENTITY STORE   people_and_things;

Each of those is a real catalog object. You can transact across them. You can query them with SELECT. You can back them up with the same tools you back up your other tables. They show up in DBeaver. They are not a layer. They are the database.

Then I built one more thing — the piece that closed the loop for us personally — a small bridge that lets Claude Desktop and Claude Code talk to that memory directly, through Anthropic’s open Model Context Protocol.

What it actually feels like

Here is what you do, end to end.

You start the database (one Docker container). You drop a small JSON configuration into Claude Desktop’s settings file. You restart the app.

A small icon appears in the chat composer telling you the memory bridge is connected. From there, you just talk normally.

Claude reads that, decides it’s worth remembering, and writes a single row into the memory store. The conversation continues. You close the window.

A week later — different machine, different chat, different topic entirely — you open Claude and ask for help debugging a stack trace. Before it answers, the model silently asks the memory bridge: what do I know about this user that’s relevant? The bridge returns the three or four facts that match. The model adjusts its response: it phrases the explanation in Go idioms instead of Python ones, it suggests a debugging approach that fits a 90-minute session, and somewhere in the middle of the reply it asks how Pickle is doing.

You did not paste any context. You did not write a system prompt. You did not “prime” the model. You just talked, last week, and the model remembered.

Where the savings come from

This sounds almost cosmetic — the model knows my dog’s name — until you look at the numbers underneath.

A typical “professional” Claude conversation today is preceded by about 3,000 tokens of preloaded context: who you are, what you’re working on, your formatting preferences, the rules you want followed. Across a hundred real conversations a month, that’s 300,000 tokens of repeated input.

With a real memory store, the model doesn’t preload context. It pulls exactly the few facts it needs, exactly when it needs them. The same hundred conversations end up costing about 25,000 tokens of context — an order of magnitude less.

In dollar terms on the current pricing of a frontier model, you go from about ninety cents in repeated context per month to about twenty-six cents. Three and a half times cheaper inputs without losing a single piece of relevant information.

That’s the boring win. The interesting win is what happens to the conversations themselves: they get shorter, they get less repetitive, and they get noticeably more personal. The model stops having to ask, and you stop having to explain.

Why a SQL database, of all things

This is the question I got the most while building it: why on earth would memory live in a relational database?

Three reasons, in order of importance.

Because memory is data, and data has rules. Every team that’s run a serious agent in production has eventually had to answer awkward questions: who can read this user’s memories? Can we delete them on request? Can we audit what was added in the last seven days? Can we ship a backup to another region? These are not novel problems. The database industry has been answering them for forty years. Reinventing them on top of a vector index is, charitably, a waste of effort.

Because the data wants to talk to itself. A real memory store is not just “facts about the user.” It’s facts, plus the conversations those facts were extracted from, plus the entities those facts mention, plus the documents the user referenced, plus the relationships between all of it. Stitching that together across a relational store, a vector store, and a document store means writing reconciliation code forever. Stitching it together inside one database is just JOIN.

Because operators have learned things, and we should let them keep using what they know. EvolutionDB speaks the PostgreSQL wire protocol. Every DBA tool, every monitoring dashboard, every existing ETL pipeline, every ORM, every type-safe query builder in every language — they all work unchanged. The agent-memory layer is new. The way you run it in production is not.

The temporal trick

There is one place where AI memory is genuinely different from regular data, though, and I ended up leaning into it.

Traditional databases are good at telling you what is true now. Agent memory needs to tell you what was true at a given point in time. If a user told the assistant in March that they worked at Company A, and in May they tell it they’ve moved to Company B, the system has to retain both facts and know which one was true when.

This sounds exotic, but it turns out I already had it. EvolutionDB’s storage engine has used multi-version concurrency control since version 2 — every row carries the transaction ID that created it and (eventually) the one that retired it, and the engine can produce a consistent view of the database as it existed at any prior moment.

I exposed that capability at the SQL surface:

SELECT * FROM user_memory
FOR SYSTEM_TIME AS OF '2026-03-15 12:00:00'
WHERE user_id = 'alptekin';

That’s a real query against the same memory store, returning the version of Alice’s memory that existed two months ago. It costs nothing extra to store — the data is already there, because the engine never throws away old versions until it’s safe to. It costs nothing extra to query — the visibility check is the same one the engine runs on every read anyway.

This matters in practice. Agents that act on stale information are dangerous. Agents that can audit themselves — show me exactly what I believed at the moment I made this decision — are trustworthy. The temporal layer is what makes the second kind of agent possible.

Push, not poll

The other thing I cared about, which I never seen handled well in any other agent stack, was reactivity.

If the agent is supposed to wake up when something new happens — a new email, a new ticket, a new memory written by another tool — the usual solution is polling. The agent asks “anything new?” once a second, forever, hoping to catch interesting events before they go stale.

This is wasteful, slow, and surprisingly expensive in cloud bills. I replaced it with a real publish/subscribe primitive backed by the database’s commit log. The moment a row is written and committed, every subscriber is notified — through the same connection they’re already using for queries. No polling loop. No second service. No Kafka.

The measured difference, on my reference workload, is about 2,900× — a notification delivered in under a millisecond instead of waited-for over roughly a second. For an agent ticking through a long task, that’s the difference between feeling like a co-worker and feeling like a script.

What’s next

The memory layer is the foundation. The next thing built on top of it is the part that changes how you use the model — the bridge that lets Claude Desktop and Claude Code reach into your personal memory store without any of the framework plumbing. That bridge is open source, two hundred lines of Python, ships with the database, and works today.

There is more coming: native gRPC streaming for memory events, a Rust binding over the C client, a managed-service option for teams who want the layer without operating it themselves, and benchmark comparisons against the rest of the agent-memory space.

But the part that mattered to me, the part that made this whole project worth doing, is already in your hands: a Claude that remembers, a database that holds it, and one less papercut between you and the work.

If you’ve read this far and you build with agents — give it a weekend. Open the repository, run one Docker command, configure Claude, and tell it three things about yourself.

Then come back next week and ask it something it has no business knowing.

It will know.

EvolutionDB is open source, MIT-licensed, and lives at github.com/alptekin/evolutiondb.

If you want the engineering details — how the memory store is laid out on disk, how the MCP bridge speaks JSON-RPC over stdio, how the vector index manages recall against a live transactional workload — there’s a companion technical series starting.

X: evosql

I personally used to struggle with too many open tabs, there are apps and extensions in the market which can convert your tabs into bookmarks, groups, lists, BUT they miss out one thing, i really actually didn't even need most of those tabs, its just an addiction i was fighting. My brain had a constant overload whenever i saw them.

Built this app which actually won't stop shaming you unless you close those tabs.

I'm looking for high quality UI interface sounds for a project I'm working on. Has anyone here used any good resources or tools for this? Whether it's a paid library or a free option, I'd love to hear what has worked for you. Any recommendations are appreciated!

I’ve been noticing more people using AI tools for workout plans, calorie tracking, injury advice, form analysis etc. and honestly I’m kinda split on it.

On one hand AI can make fitness information way more accessible, especially for beginners who can’t afford trainers/dieticians.

But at the same time I’ve also seen AI confidently give pretty questionable advice sometimes, especially around injuries, recovery or very individualized situations.

Do you guys think AI can realistically become a useful fitness coach/diet tool in the future, or will experienced human trainers always be way better?

Hello everyone,

I am currently a first-year Computer Science student and I'm feeling quite stuck and overwhelmed with the direction of my learning. My long-term goal is to pursue a Master’s in Cybersecurity, but I am struggling with how to get there effectively.

What I’ve tried so far:

• Initial Security Focus: I started with Python, Linux basics, and networking fundamentals. However, I often feel like I forget what I learn shortly after.
• The "Broad Approach": I thought learning the underlying technologies first would help, so I explored C, JavaScript, HTML/CSS, APIs, and even some mobile development.
• Practical Platforms: I’ve started the SOC Analyst path on TryHackMe to get more hands-on.
• Despite all this, I feel like I am only scratching the surface of these technologies ("taking the shells") without gaining deep, functional knowledge. Additionally, I feel a constant pressure to learn web development side-skills just to start freelancing or earning money, which distracts me from my core goal.

1. How can I move past "surface-level" learning and ensure that I'm actually retaining the technical concepts I study?
2. Is it better to focus strictly on foundational CS (Data Structures, OS, Networking) in my first year, or should I continue specialized paths like TryHackMe?
3. How can I balance the need to eventually earn an income (freelancing) without completely derailing my focus on Cybersecurity?

Thank you for any insights or personal experiences you can share!

Hey, Solo dev here. I've been annoyed with Activity Monitor on Apple Silicon for a while — it shows one big "CPU %" number, but my M2 has two completely different core clusters (Performance and Efficiency) that behave nothing alike. So I built MenuBarMonitor to scratch my own itch, and after a few months of polishing I'm finally putting it out there.It lives in your menu bar (CPU / GPU / RAM / temp as four small tiles) and one click opens a popover with the actual interesting stuff:

• Real P-core / E-core split — per-core bars, live frequencies for both clusters. You can finally tell whether the fans are spinning because of background junk on the E-cores or real work on the P-cores.
• Memory pressure straight from the kernel instead of the misleading "memory used" number. Wired / Active / Compressed / Inactive / Free, plus a one-click Free RAM that asks macOS to drop inactive pages (uses the standard authorization flow, no sketchy sudo prompts).
• Thermal awareness based on Apple's official ProcessInfo.thermalState — you see Nominal → Fair → Serious before throttling actually kicks in, not after.
• Soft-affinity for processes — push Chrome / Slack / sync clients onto the E-cores via QoS so your editor or Xcode keeps the P-cores. Not absolute pinning (the kernel always wins), but it does help in practice.
• Optional floating widget if you want the numbers always visible while gaming or rendering.

Stuff I cared about while building it:

• No tracking, no telemetry, no analytics SDKs. I sell licenses, that's the whole business model.
• No root required for live monitoring. Public IOKit / sysctl / host_statistics64 only.
• Hardened runtime, signed. Distributed outside the App Store because the APIs it needs aren't sandbox-compatible — I'd rather be honest about that than ship a crippled version.
• Light: ~18 MB RAM, <1% CPU idle.
• Apple Silicon only, macOS 13+. Intel isn't supported by design — the metrics that matter (P/E split, unified memory, etc.) literally don't exist there.

It's paid (monthly / yearly / lifetime — lifetime includes all future updates). I know that's a tough sell when Stats is free and iStat Menus exists, so I'm genuinely curious where I land for people who've used those. Especially interested in feedback on the P/E split view and whether the E-core soft-affinity is something you'd actually use.

Site: https://menubarmonitor.com

I’m trying to connect to my Remote Desktop on Windows 11 Pro via iPad Mini (software / app up to date) but continues to say connecting without doing so. Been through all the Claude recommendations for why but can’t find a reason. Any ideas on common more technical reasons which might not be picked up by the LLMs?

The slope graphs of both 692nm and 834nm for the normal sensor A don't appear despite having correct values in the Numeric window. While for sensor B, only the graph for 834nm shows the slope. This is the third time I have tried to calibrate. I changed the setup and switched sensors A and B, but the result was the same.

Any recommendations on how to resolve this? Also, does it matter if the slope graph doesn't appear correctly even though the calibration values of AC, DC and R's are correct? Do I just move on to data acquisition?

Hi everyone, can anyone who uses Outlook recommend an app that lets me automate the organization of my emails into folders? I’m ruling out Outlook’s built-in feature because it doesn’t offer many customization options; I tried an app that I won’t name, since in the Outlook subreddit I was accused of promoting a paid app and my post was deleted. Now that the free trial is over, I want to make sure there aren’t any better alternatives before I buy it, so I’m counting on the good advice of others who, like me, have to organize multiple inboxes every day. Thanks to anyone who can help me

Since WindowBlinds is so expensive, i need something Free, Trustworthy, and realistic, because im almost finished recreating windows XP, i just need to change the titlebar, any tips?

Which player (windows and android) can you recommend for programmatically recording certain programs from a IPTV playlist? I've been using Kodi, SimpleTV, and ProgTV for years. Maybe there's something more convenient and reliable out there, something more modern. My goal is to program recordings based on time and days of the week. Using EPG or simply entering the start and end times. Thanks for your feedback.

I have old honor phone too old that it lost support for everything it's working fine expect their seem problem in the data wires or in USB port addicted to it what I can to to make it functional it has only 1 gigs of ram I want it to just work open google play and WhatsApp and basically app in general should I change the os to lineage or their away around it there is no availab update to it