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Hey guys. I am a GPT Plus user, and I use Freebuff a lot to execute my tasks for free, so I don't use up usage limits at all. Freebuff, if you don't know, is a desktop and CLI agent that gives u a bunch of models for free (DeepSeek V4 Flash, GLM 5.3 Flash, 5.6 Luna, Solar 4 Pro), and it is really good at executing tasks that you give it, imo. I searched for connectors or MCPs that connect to it so I can seamlessly integrate it with Luna or Terra as the planner and Freebuff as the implementer. There was nothing online, so I created my own MCP ( https://github.com/Praket7/freebuff-mcp ). If you guys could check it out, try it out, and let me know if I need to make any security changes or to make it work better, and if you guys could star and test it, I'd appreciate it. I am currently adding some more features, like ChatGPT or Claude being able to check live progress, but let me know if you would like something else or if it doesn't work. thanks! submitted by /u/Swimming_Ask3859 [link] [留言]

The UK's National Cyber Security Centre says employees using AI tools outside an organisation's approved systems can expose company or customer data and reduce the organisation's visibility and control over that information. It cites research saying 71% of employees use AI tools that their employer has not approved. The NCSC also warns that AI agents add another risk: if an agent has a vulnerability or bad configuration, an attacker may gain the same data, services, and privileges the agent can access. The practical point is less 'ban AI' and more 'make the approved path usable'. The NCSC says teams should understand why people use shadow AI, provide safer alternatives, and reduce the risk rather than assume it will disappear. Sources: https://www.ncsc.gov.uk/blogs/the-hidden-risks-of-shadow-ai https://ukstories.microsoft.com/features/rise-in-shadow-ai-tools-raising-security-concerns-for-uk/ submitted by /u/Codeblix_Ltd [link] [留言]

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

submitted by /u/beingmodest [link] [留言]

AI can give you more working hours than there are hours in your day. That does not mean it gives you more finished work. On September 6, 2026, OpenAI published a detailed look at how coding agents are changing work inside its research organization. One number will get most of the attention: by mid-August, the organization was using 3.1 agent-workdays of runtime for every human workday . That sounds like somebody installed an extra Monday, Tuesday, and Wednesday inside Monday. OpenAI also reported that researchers were contributing code faster and running more experiments. Agent use had expanded beyond writing research and infrastructure code into technical help and monitoring runs. Some internal support office hours saw less demand because agents were handling troubleshooting work. But the report makes an important qualification: faster code and more experiments do not automatically make the whole research process 3.1 times faster. Research includes deciding what to pursue, designing experiments, running them, analyzing results, communicating findings, allocating compute, catching failures, and applying safety controls. Speeding up one stage can simply move the waiting line somewhere else. That is the useful lesson for a freelancer, solo founder, or beginner building an app with AI: Do not ask whether you are using enough AI. Ask which stage is limiting finished work. I call the tool for answering that question a bottleneck map. The beginner mistake: measuring the assistant instead of the work AI tools make activity easy to see. You can count tokens, prompts, agent sessions, generated files, commits, pull requests, tests, or hours of runtime. Those numbers can help with cost and capacity planning. They are terrible substitutes for the result your customer or user needs. OpenAI's own report is careful here. The organization observed more code and more experiments, but it also said those metrics are easier to measure than their relationship to research progress. As au

I Have a Job — I Just Need to Do It A few months ago, I left my last job as a remote Unity developer. Before leaving, I had already started working on a freelance project to build a multi-tenant security and workforce management system . It became a fairly large system involving web applications, mobile apps, real-time tracking, scheduling, reporting, GPS, notifications, and more. The project is now mostly completed, but the client wants to continue adding maintenance, business logic changes, UI modifications, and new development under the same maintenance fee. That doesn't work for me. Maintenance and development are two different things, and when the amount of new development keeps growing while the price stays the same, eventually it stops being sustainable. So I started thinking about what I would do next. The Fear of Not Having a Job For the last few days, I was genuinely worried. I have more than 250,000 BDT in savings , so I'm not in an immediate financial crisis. But money slowly disappears when there is no income. And freelancing isn't exactly comforting right now either. I've been using Upwork, but the experience has become increasingly frustrating. You apply for jobs and often hear nothing. Some clients post a job and never hire anyone. Some jobs get dozens of proposals and disappear quickly. Some invites arrive, but someone else gets hired almost immediately. And every application costs money. After a while, it starts feeling like you're continuously putting money into a machine that promises a job somewhere in the future. You keep applying. You keep waiting. You keep hoping. And eventually, I realized something. What I Was Actually Missing I wasn't missing money. I was missing a job . And there is an important difference. I already have the skills. I already know how to build software. I already have ideas. I already have projects I want to work on. I was simply thinking that a "job" had to come from someone else. Then I thought: I can create my own job

I recently implemented the Karatsuba multiplication algorithm on a 8-bit TTL computer I hack on. In this video I explain the algorithm, and explore the speed benefits of using it. submitted by /u/MichaelKamprath [link] [留言]

Welcome to part one! This is the start of a series where I’ll be posting about my webdev and HTML nightmares. I hope you enjoy the read as much as I hate User Interfaces! Consider this a shared space for learning—I’m sharing what I’ve learned so far, and I’d love to hear your thoughts or better solutions in the comments. To kick things off, let’s talk about how this whole mess started. As a solo developer, you want to spend 99% of your time actually building the things you love. So when it’s time to share builds with early playtesters, I naturally take the path of least resistance... a pinned link in a Discord channel and a shared Google Drive folder. And for a while, it works. Until it suddenly doesn't. The Problem: The "Easy way" Trap Privately, with a small group of alpha testers, Discord is great. You can pin messages, create specific channels, and guide people directly. But as soon as you want to go public, Discord becomes a nightmare for onboarding new users: The "Tutorial" Requirement: If a new user needs a 5-minute guide just to navigate your Discord server to find the launcher or the latest release, you’ve already lost them. Zero Discoverability: Discord is great for community and chat, but terrible as a public storefront or documentation hub. Searching for news, filtering updates, or finding launcher links creates massive friction. Lack of Professionalism: To offer real support, showcase features, and look trustworthy to a public audience, you need a single source of truth—not a maze of text channels lost to the void. I didn't have time to manage an overly complex custom web setup or pay high monthly SaaS fees, but I needed a clean, low-maintenance way to go public. Yes, I spent no more than thirty seconds drawing this on my Bamboo tablet: Why Google? (And the Launcher Evolution) Before even thinking about the website, I had to solve the distribution problem for my launcher. I experimented with several download pipeline prototypes: Git Repos / Diversion (f

Everything you need to know to decide which set of Sony earbuds are right for you.

The short version NextAuth (now Auth.js) creates 4 tables in your database: users , accounts , sessions , and verification_tokens . The users and accounts tables have a one-to-one relationship via accounts.user_id . Sessions link to users via sessions.user_id . Verification tokens are short-lived and self-cleaning. The 4 tables users Column Type What it means id text / UUID Primary key. Generated by NextAuth. name text Display name from the OAuth provider (Google, GitHub, etc.) email text User's email. May be null if the provider doesn't share it. email_verified timestamp When the email was verified. Null if never verified. image text Profile picture URL from the provider. created_at timestamp When the user first signed in. updated_at timestamp Last profile sync from the provider. accounts This table links a user to an OAuth provider. One user can have multiple accounts (e.g., Google + GitHub). Column Type What it means id text / UUID Primary key. user_id text Foreign key → users.id . type text Always "oauth" or "oidc" . provider text "google" , "github" , "discord" , etc. provider_account_id text The provider's unique ID for this user. refresh_token text OAuth refresh token (encrypted in production). access_token text OAuth access token (encrypted in production). expires_at integer When the access token expires (Unix timestamp). token_type text Usually "Bearer" . scope text Permissions granted by the provider. id_token text OIDC ID token (if using OIDC). session_state text Provider-specific session state. sessions Active sessions for each user. NextAuth creates a new row here on every sign-in. Column Type What it means id text / UUID Primary key. session_token text The session token stored in the user's cookie. user_id text Foreign key → users.id . expires timestamp When this session expires. verification_tokens Short-lived tokens for email verification, password reset, etc. Self-cleaning old tokens are deleted automatically. Column Type What it means identifier te

While it probably won't be available in the US, the Audi A2 e-tron looks like an appealing affordable EV with plenty of range.

I was testing out CH-Ops - an admin GUI for self-hosted ClickHouse - on a simple setup: 1 shard, 2 replicas. Stumbled onto the replication queue almost by accident. Here's what I did: I stopped one of the nodes (let's call it Node B), then inserted some data through the other one (Node A). Just wanted to see what would happen. Then, while Node B was still down, I checked it in CH-Ops. It had stuff sitting in its replication queue. My first assumption was: okay, this must be showing what's left to replicate across the cluster - the total pending replication work. So I switched over and checked Node A, the one that was actually up and had just received the insert. Its queue was empty. That didn't match what I expected at all. If the queue was a cluster-wide "here's what still needs to replicate" view, Node A should've shown something too - it was the one that had the fresh data now waiting to reach Node B. Instead it was Node B, the down one, sitting there with pending tasks. That mismatch is what sent me digging. Turns out the queue isn't cluster-wide at all - it's specific to each ClickHouse instance. Once I brought Node B back up, its queue drained in seconds and the data showed up. That whole experiment is basically the entire post in miniature. Here's the mental model I ended up with. A Queue Belongs to a Replica, Not to the Table This is the first thing to get straight. With a ReplicatedMergeTree table, you can have multiple replicas holding copies of the same data. It's tempting to think of replication as one shared pipe between them. It isn't. Each replica keeps its own local replication queue . So if you see: Replica 1 → queue_size = 0 Replica 2 → queue_size = 25 that doesn't mean 25 operations are waiting somewhere in the middle for both replicas to pick up. It means Replica 2, specifically, has 25 tasks it hasn't finished yet. Once that clicked for me, the rest of the system made a lot more sense. So Where Do These Tasks Come From? Replication in ClickHouse

My system prompt had an example of a good Slack message in it. It opened with "Morning all, quick one:". The model started opening real Slack drafts with that exact phrase. Then it started saying "Morning." when I typed "hey", which is a small lie, because it cannot see a clock. So I added a rule telling it not to reuse examples from its own instructions. Three rebuilds. No change. Then I deleted the phrase. Fixed on the next build. That is when it clicked. The model does not read your system prompt as a list of instructions. It reads it as text that is likely to appear near its own output. Every finding below falls out of that one idea. The four rules I now write prompts by If a phrase must not appear in the output, it must not appear in the prompt. Banning it does not work. Deleting it does. Naming a bad example summons it. "Not the bank balance one" is an excellent way to get the bank balance one. Position beats wording. A rule buried mid-section gets read and traded away. The same words at the top of that section hold. Concrete beats principled. "Call fsync() before the rename" lands immediately. "Describe only the guarantee the code actually makes" does nothing. And the one that saved me the most time after it cost me the most time: verify on three seeds before you believe any of it. Here is the evidence for each. The setup Flash Onyx is the model line behind Flash , my local agent shell. There is no fine-tuning involved. Onyx is a base model plus a system prompt that has grown to roughly 680 lines, built into an Ollama tag with a small script: python3 models/build.py models/flash-onyx-2.5.Modelfile --size 31b-cloudbase -n Natuworkguy 2.5 is the version where I stopped editing that prompt by feel. The loop is not clever: edit the prompt, rebuild the tag, run a fixed set of prompts at pinned seeds, read the output, decide whether anything actually changed. Seeds are pinned so two runs are comparable. That is the entire method, and it is the difference between "t

I had a table in the database that was supposed to fill itself. Its job was to learn from failures : every time the system tried a variant of something and it didn't work, it saved it, to recycle later in another context where it might. A laboratory of failed attempts, piling up. In production it had zero rows . It had been deployed for days and hadn't saved a single record. Meanwhile a neighbouring table —another memory, the one that notes which work is already exhausted so as not to repeat it— was growing normally. The temptation is obvious: there's a bug in the write. I went looking for it, and it wasn't there. Zero rows isn't the same as a write error The path that saves into that table emits a warning if the write fails. I searched the logs for those warnings: zero . No write had failed. That's a fact, not an absence of one. If the path had been taken and had failed, it would have left a trace. Zero traces and zero rows fit only one explanation: the write path never ran . Not ran-and-failed. Didn't run. That's the difference between a real negative and a negative that was never put to the test, and they look the same unless you look for the positive control —something the log WOULD show if the path had been taken—. Without it, "healthy and quiet" and "dead" look identical. Two mechanisms starving each other Why didn't it run? Because of another mechanism, upstream, doing its job well. That system has a negative memory : when it exhausts everything it knows how to try against a target, it notes it down, so as not to spend effort again on something it already knows won't pay. It's a sensible optimisation. But it sat before the phase that generated new variants —the phase that, on failing, would have fed the library—. As soon as a target went "exhausted", that phase was skipped entirely . And if the phase never runs, it never produces a failure to save. Each mechanism, on its own, is correct. The negative memory avoids useless work. The library learns from failure