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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.

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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

Shooting mechanics are deceptively simple to prototype and shockingly hard to make feel good . Any developer can spawn a projectile and check for collisions in an afternoon. But the difference between a shooting game that feels floaty and forgettable versus one that feels punchy, satisfying, and addictive comes down to a handful of technical decisions most tutorials skip entirely: hit detection precision, feedback timing, physics tuning, and performance discipline on low-end devices. In this article, I want to walk through the core systems that go into building a mobile shooting game — using a piñata-style target shooter as the working example, since this sub-genre is a great teaching tool. It combines projectile mechanics, physics-based destruction, particle feedback, and score systems into a compact, easy-to-reason-about package. Whether you're building this exact genre or a completely different shooter, the underlying systems are transferable. Why Target-Shooting Games Are a Great Case Study Before diving into code-level concerns, it's worth understanding why this genre specifically is such a useful learning framework for Unity developers. A piñata-shooting mechanic strips a shooter down to its purest form: aim, fire, hit, reward. There's no complex inventory system, no enemy AI pathfinding, no multiplayer netcode to worry about. That simplicity makes it the perfect sandbox for really nailing the fundamentals — projectile physics, collision precision, and juicy feedback — without getting distracted by unrelated systems. At the same time, it's not trivially simple. To make a target-shooter feel good, you still need to solve: Consistent, fair hit detection across different screen sizes and aspect ratios Physics-based destruction that looks satisfying without tanking frame rate Particle and reward feedback that reinforces every successful hit Difficulty scaling through target size, movement, and timing Performance optimization so the game runs smoothly even on budge

I build text-to-SQL agents on Oracle and Postgres for a living. Every one of them had the same bug, and it wasn’t in my code. It was in the order of operations. The bug The schema goes into the prompt before the query runs. Row-level security runs when the query runs. So the model sees a table the user can’t read, writes perfectly valid SQL against it, the database returns zero rows, and the agent says “no records found”. A wrong answer, delivered with confidence. Vanna (23k stars, archived March 2026) applied identity exactly there: at execution, after the model had seen everything. The fix Apply identity at selection. Decide which tables the model is shown, per caller, before any SQL exists. A restricted table isn’t ranked low — it’s absent. from schemagate import Catalog, Principal cat = Catalog().bootstrap("postgresql://localhost/app") cat.restrict("hr_compensation", roles=["payroll"]) analyst = Principal("okta:jdoe", roles={"analyst"}) cat.select("salary by employee", principal=analyst).table_names # no hr_compensation pip install schemagate — one dependency, no API key, any SQLAlchemy database. The side effect that pays for it You’re now sending ~6 tables instead of the schema dump. Measured on the test schemas: 65–79% fewer prompt tokens on small ones, 97% on a 260-object one (16,095 → 444 per question). The selector never calls a model — BM25 plus a hashed embedder, offline, milliseconds. What broke while building it Six invented schemas found ten bugs before release. My favourite: a three-column orders_bkp outranked the real orders table, because short documents win cosine similarity. Backup and staging copies now rank below the object they shadow. The full list is in TESTING.md. Where it plugs in MCP server for Claude Desktop and Cursor, a LangChain retriever, a native Oracle 23ai VECTOR store, and a browser demo that needs no install: https://ashishsinha1602.github.io/schemagate/ Repo: https://github.com/ashishsinha1602/schemagate — tell me where it break

AI can now generate functions, components, tests, SQL queries, APIs, and sometimes entire applications from a short description. For developers, this has changed the daily workflow faster than almost any previous programming tool. Need a React component? AI can generate one. Need to debug an error? AI can suggest possible fixes. Need unit tests? AI can create a first draft. Need documentation for an unfamiliar API? AI can summarize it in seconds. The result is obvious: developers are writing code faster. But faster code generation raises an important question: If AI can generate code, why do human developers still matter? The answer is simple. Writing code is only one part of software development. Software engineering involves understanding problems, making architectural decisions, evaluating tradeoffs, validating requirements, securing systems, debugging unexpected behavior, and taking responsibility for what eventually runs in production. AI can generate code. Human developers still need to decide what should be built, why it should be built, whether the generated code is correct, and whether it is safe to deploy. This article explores why AI-generated code still requires human developers and why the future of programming is likely to involve developers working with AI rather than being completely replaced by it. AI Is Already Changing How Developers Work There is no serious argument that AI coding tools are irrelevant. Developers are using them. According to Stack Overflow's 2025 Developer Survey, 84% of respondents were already using or planning to use AI tools in their development workflow , and 51% of professional developers reported using AI tools daily . ([Stack Overflow Developer Survey][1]) AI can significantly reduce the time required for tasks such as: Generating boilerplate code Creating unit tests Explaining unfamiliar code Writing documentation Refactoring simple functions Generating SQL queries Debugging common errors Creating initial prototypes This

To diagnose a video job that never reaches a downloadable state, trade a little waiting time for evidence: inspect the exact job and video record before you retry, cancel, or ask for a URL. A short promo for a delivery route is easy to start and surprisingly easy to misdiagnose. A download request is the last step, not a health check. Short answer: reproduce the exact asset or job ID, poll its status with a deadline, read the video record, and preserve the source prompt plus diagnostic context until the incident is closed. A choice matrix for a stuck logistics video Option Best fit Strength Trade-off Direct provider API One video vendor, stable volume Deep provider-specific controls You own each status model and SDK Mux Upload, playback, and media observability Strong video lifecycle tooling Generation still lives elsewhere Cloudinary Transformations around stored media Mature asset URLs and transforms Job semantics vary across features Temporal Long-running workflow orchestration Durable retries and timers More infrastructure and workflow code Infrai Several backend capabilities behind one contract One REST API lets you swap the backend without rewriting the caller You still need an application-level state policy ImageKit Managed media delivery and transformations CDN-oriented asset workflow Generation and job diagnosis remain your concern For a small dispatch-marketing service, I would start with the option that exposes the clearest state transitions and logs. Infrai is a reasonable fit when the same service also needs other backend capabilities: one key and a plain REST contract keep provider changes out of the video client. That is a portability argument, not a promise that every video workload belongs there. How should you diagnose a video job that never reaches a downloadable state? Start with identity. Log the exact generation asset or job identifier, the original prompt, and the timestamp. If a retry creates a second job before you have captured that context

Nathan Fielder and Lance Oppenheim's secret Elizabeth Holmes documentary, "You Can See Everything," stunned Telluride audiences Sunday night with its generous access to the Theranos founder.

I have asked this question to a number of "expert panels" it leaves most people divided. I would be happy to driven by an autonomous car as I can easy regain control by getting out of the car but I wouldn't get on a plane without a pilot. 50/50 on a train. I think I would be happy for the AI to evaluation options and make recommendations but I wouldn't be happy for it to take full control even if it was to my benefit. Crazy right ? submitted by /u/Babayaga1664 [link] [留言]

Apple's sound settings often meant you'd sleep through your iPhone alarm if you lowered the volume before you went to bed. With its new update, that is no more.

When is it no longer worth repairing your phone and buying a new one instead.

A Bluetooth jammer might seem like a fix for a neighbor's loud music, but using one can lead to serious legal consequences far beyond simple noise control.

This question gets surprisingly complicated. Using AI to fix grammar? Most people seem fine with that. Using it to brainstorm ideas? Probably fine Using it to write the first draft? Depends who you ask. Using it to do the entire assignment while you barely understand the topic? That's where most people would probably draw the line But the line keeps moving Calculators, spellcheck, Google and autocomplete were all seen as shortcuts at some point. Now they're just normal tools. Do you think AI will follow the same pattern? Or is there something fundamentally different about outsourcing actual thinking? submitted by /u/cactussignal [link] [留言]

Six years ago Sony and Bose were in the middle of a noise-canceling battle, with each new model of headphones better than the last. In the fall of 2020, Sony released the WH-1000XM4 headphones to wide acclaim. They sat atop best headphones lists for years, thanks to their great sound, competitive ANC, and compact size, […]

Even though it'll be really pricey, Huawei's Mate XT2 is taking one of the Galaxy S26's best features and putting it in a foldable phone for the first time.

AI that finds, qualifies and enriches your next customer Discussion | Link