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Roku blames RAM shortage after CEO called it "great" for business in May.

Meta's smart glasses have been a PR headache for the company. Public backlash has been swift, and fierce; people are concerned about the erosion of privacy and expansion of surveillance. Some especially bad actors are using the glasses to film themselves "pranking" random strangers. Women have become unsuspecting social media content for men filming themselves […]

Qualcomm sent a letter to customers on Friday warning of plans to increase its prices by "a percentage in the double digits," Bloomberg reports. The price hikes will go into effect starting with products shipped after September 1st. Qualcomm claims it has "exhausted its ability to absorb higher costs from suppliers," as ongoing component shortages […]

Midjourney, which has gone from generating AI cat images to full-body ultrasound scans, is getting into a new field: astrology. The AI startup announced on Thursday that it has acquired the personalized astrology app Co-Star, as reported earlier by Bloomberg. Co-Star is a free app that offers daily horoscopes and allows you to check your […]

Tool comparison posts obsess over feature matrices and monthly pricing. Both are the easy numbers. The expensive number is what it costs to leave , and almost nobody publishes it. Three kinds of lock-in, in ascending order of pain Data lock-in is the one people check. Can you export? In what format? A CSV dump that loses your relationship structure is not really an export. Workflow lock-in is worse and less visible. Your team learned the tool's mental model. Your runbooks reference its UI. Your onboarding docs have screenshots. Switching means rewriting all of that, and none of it shows up in a pricing comparison. Integration lock-in is the killer. Every webhook, every CI step, every Zap pointing at this tool is a thing that breaks on migration day. The count grows silently — nobody tracks how many integrations a tool accumulates until they try to remove it. A rough way to score it before you commit Before adopting anything, ask four questions and write the answers down: Export fidelity — can I get my data out in a form a competitor can actually ingest? Not "is there an export button." Integration surface — how many other systems will end up pointing at this? Each one is future migration work. Config as code? — if the configuration lives in a database behind a UI, migration means clicking. If it lives in YAML in my repo, migration means editing files. Who owns the identity? — if the tool is also your auth provider, leaving is a much bigger project than swapping a dependency. Score each 1-5. A tool scoring badly on 3 and 4 needs to be substantially better to justify adoption, not marginally better. Why the cheap option often is not The pattern I keep seeing: a team picks the cheaper tool, accumulates 20 integrations over 18 months, then discovers the migration cost exceeds three years of the price difference they were optimising for. Pricing is a recurring cost you can forecast. Migration friction is a one-time cost you cannot, and it lands at the worst possible mome

Every "run this model locally" guide tells you to grab a Q4 GGUF and move on. That advice is fine right up until you try a long-context run and your machine starts swapping. The weights are the part everyone budgets for Quantization maths is straightforward. A model's weight footprint is roughly params x bits / 8 : Quant Bits/param 12B model Quality note Q8_0 ~8.5 ~12.8 GB Near-lossless, rarely worth it Q6_K ~6.6 ~9.9 GB Very close to Q8 Q4_K_M ~4.8 ~7.2 GB The usual sweet spot Q3_K_M ~3.9 ~5.9 GB Noticeable degradation Below Q4 the loss stops being subtle. Instruction-following degrades before raw perplexity does, which is why benchmark numbers can look fine while the model quietly stops respecting your system prompt. The KV cache is the part that bites Here is what the guides skip. The KV cache scales with context length , and it is not quantized by default: kv_bytes ~= 2 (K and V) x layers x kv_heads x head_dim x seq_len x dtype_bytes The practical consequence: a model that loads in 7 GB can need well over twice that at long context. Grouped-query attention helps a lot — kv_heads is much smaller than attention heads — but the term still grows linearly with sequence length while your weights stay fixed. Two knobs matter more than picking a fancier quant: --ctx-size : do not allocate 128K if your prompts are 8K. You are reserving memory you will never touch. KV cache quantization ( q8_0 for K/V): roughly halves cache memory for a quality hit most workloads never notice. Underused. A decision order that works Start at Q4_K_M Set context to what you actually use, not the model maximum If you are still tight, quantize the KV cache before dropping to Q3 Only move up to Q6/Q8 if you have headroom left over That ordering matters: dropping to Q3 to buy context is the most common mistake, and it trades a permanent quality loss for memory you could have gotten from the cache instead. Per-quantization benchmarks and deployment notes for the Gemma 4 family are collected at ge

The eight-episode series stars Michelle Yeoh and Hunter Schafer.

A normal secondary index can answer: status = "active" It cannot efficiently answer: documents containing "distributed database" LioranDB therefore has a dedicated text-segment architecture. Tokenization Text is split on non-alphanumeric characters. Depending on index options, tokens can be normalized to lowercase and filtered through stopwords. "Building Distributed Databases" becomes ["building", "distributed", "databases"] Segment contents A LioranDB text segment can contain several files and structures: Term dictionary Posting lists Document map Document-length norms Optional term positions Bloom filter Segment metadata A posting connects a term to the local documents containing it. "database" → [doc 2, doc 8, doc 19] Positions can record where the term appears inside each document. That enables more advanced query behaviour and phrase-aware features. Global and local document IDs Each segment assigns compact local IDs to its documents. A separate document map translates them back to global document IDs. This keeps postings smaller while preserving the external identity of the record. Bloom filters Each segment also maintains a Bloom filter for terms. Before reading a segment's postings, the query path can test whether the term might exist there. A negative answer is definitive. A positive answer means the segment may contain the term and should be checked. Query modes The text query layer supports modes such as: AND OR It also emits scored documents and metrics including: Query time Postings read Candidate documents Segments searched Full-text search is essentially a specialized database living beside the document database. Its data structures, compaction behaviour, scoring, and caching needs are different enough that treating it as a plain secondary index would be a mistake. Built by Swaraj Puppalwar under Lioran Group . Learn more: LioranDB Lioran Developer Solutions Lioran Group

Why Sick Patients Hate Your Cheerful Conversational Voice AI Picture an oncology patient sitting in the dark at three in the morning, nursing a severe bout of breakthrough pain. Desperate for assistance, she calls her clinic's scheduling and triage line. Instead of a calm, grounded response, she is greeted by an artificially bright synthetic voice: "Hi there! What a bright day to take care of your health!" The patient hangs up immediately. This reaction is far from an isolated incident. When individuals reach out to a medical office, they are rarely seeking entertainment or cheerful banter. They are frequently managing acute pain, administrative frustration, or intense health anxiety. When a high-stress emotional state collides with a forced, chipper baseline tone, the resulting healthcare voice AI tone mismatch creates severe cognitive friction. Patients perceive forced cheerfulness as cold apathy disguised as friendliness. In clinical communications, this phenomenon manifests as conversational AI toxic positivity. An upbeat virtual receptionist telling a patient with severe chest tightness that it would be "happy to help you today" projects a disturbing lack of situational awareness. Instead of humanizing the interaction, artificial warmth highlights the machine's non-human nature. It pushes the caller deep into the uncanny valley of simulated care, leaving patients feeling managed by a cost-cutting algorithm rather than supported by a dedicated care team. The Data Behind Patient Frustration Industry benchmarks reveal a profound disconnect between healthcare automation strategies and patient expectations. While health systems rapidly deploy patient experience virtual assistant models to offload administrative burden from front-desk staff, few organizations audit the emotional resonance of their automated telephony systems. Metric Patient / Consumer Reaction Source 68% Report heightened frustration when automated healthcare voice systems use overly enthusiastic or

This climate phenomenon could cause a massive global temperature spike and cause economic losses of $10 trillion by 2032.

Disk structures are durable, but updating them for every write is expensive. LioranDB uses memtables to absorb writes before flushing them to the on-disk B+ tree. What is a memtable? A memtable is an ordered in-memory map. In LioranDB, each entry contains either: Value ( bytes ) or: Tombstone A tombstone represents a deletion. The memtable also tracks: Approximate memory usage Minimum LSN Maximum LSN Entry count Put count Delete count The write path A simplified write path looks like this: Application write ↓ WAL durability ↓ Mutable memtable ↓ Immutable memtable queue ↓ Background flush ↓ Disk B+ tree The active mutable memtable accepts new writes. When it crosses a size limit, the engine rotates it into an immutable memtable. That immutable table is no longer modified and can safely be flushed in the background. Why ordered maps? LioranDB uses an ordered map for memtable entries. This helps because the flush process can emit keys in sorted order, which is friendly to the B+ tree and bulk-write paths. It also simplifies range merging between: Mutable data Immutable data On-disk pages Backpressure Background flushing cannot be allowed to fall behind forever. LioranDB therefore tracks limits such as: Maximum immutable memtables Maximum immutable bytes Partition-wide queue limits Maximum writer stall duration If the disk cannot drain the backlog quickly enough, the foreground write path slows down. That may sound undesirable, but controlled backpressure is much safer than consuming memory until the process dies. A memtable is not merely a cache. It is a pressure valve between CPU-speed writes and disk-speed persistence. Without that valve, the engine would either become slow on every commit or dangerously accumulate unbounded work. Built by Swaraj Puppalwar under Lioran Group . Links: LioranDB Lioran Developer Solutions Lioran Group

The first session ended at post 10. The design did not. I came back to it and, before writing a single new decision, asked the least glamorous question a solo project can ask itself: how far along is this, really, and what would it take to call it ready for someone else to review in depth? The answer was more useful than I expected, because it forced a distinction I had been blurring: a settled mechanism is not a hardened design . The fork: promote now, or hold and harden Composition was already reconciled into the binding docs. Coherence had seventeen recorded decisions covering the whole load-bearing core: binding, determinism, precedence, persona content, gender, explainability, detection, activation, the explicit accessor, and a second entity proving the abstraction generalizes. It was tempting to call that reviewable and promote it too. A: promote coherence into the binding docs now. 17 decisions, self-consistent, composition already went. Looks done. B: hold. The mechanism is settled, but the seams between features are not. Harden first, promote second. I took B. The tell was that I could not yet answer a reviewer's most obvious question, "what happens when a composed child is itself a person," without pointing at an open fork. A design you cannot stress at the seams is decided, not done. What "hardened" actually means The value of taking stock was turning a vague "almost there" into a concrete, finite list. Three passes stand between the current state and an in-depth review: 1. Cross-feature interaction pass. Where correctness bugs hide once two features exist. Composition x coherence is done (next post). Uniqueness, null-probability, and locale remain. 2. Surface-enumeration pass. Collect every public member the design has accumulated into one list to accept or cut. Public surface is locked, so this is the gate that matters most. 3. Consistency re-read. Read all the decisions straight through for contradictions and stale cross-references, the kind that creep

AI agents can reason about the web. But giving an agent unrestricted browser or network access creates a serious authority problem. The obvious solution is to restrict the tools available to the agent. Then I kept running into the opposite problem: Once the tool became sufficiently restricted, it lost many of the capabilities required to complete real work. I wanted both sides: Enough power to crawl, render, navigate, extract, capture, and investigate the web Explicit operator control over origins, credentials, budgets, browser hooks, profiles, and evidence So I built Cockroach Crawler . It is an open-source Node.js and TypeScript toolkit for AI agents, RAG pipelines, documentation indexing, research, QA, and web-data workflows. I connected it to OpenClaw and Codex , and the difference was honestly wild. Instead of giving the agents one narrow search tool, I gave them a bounded web-research layer that could crawl websites, inspect JavaScript applications, extract structured data, process PDFs, take screenshots, generate PDFs, inspect public sources, and return evidence with provenance. And for many public workflows, I did not need to configure a separate API key for every source. GitHub: https://github.com/AjnasNB/cockroach-crawler Documentation: https://cockroachcrawler.com/docs/ npm: https://www.npmjs.com/package/cockroach-crawler What changed after I connected it to OpenClaw and Codex? Before this, the agents could reason well, but their web access was limited. They could answer questions, write code, and work with the context I gave them. But once a task required deeper live-web investigation, I still had to manually combine several tools. After connecting Cockroach Crawler, they could: Crawl public websites Render JavaScript-heavy pages Follow sitemaps Search and map documentation sites Extract readable Markdown Extract structured fields with CSS, XPath, or restricted regular expressions Read local and remote PDFs Generate PDFs Take screenshots Handle bounded c

Most developers think of LioranDB as a document database. Internally, however, its storage engine does not understand documents, objects, fields, or JSON. It understands only: table + key bytes + value bytes That separation is intentional. The architecture LioranDB is split into two major layers: Application ↓ Document DBMS ↓ Transactional key-value engine ↓ WAL, memtables, B+ tree, pager and disk The engine exposes operations such as: get ( table , key ) put ( table , key , value ) delete ( table , key ) scan ( table , range ) The DBMS layer then adds document-oriented features: Collections JSON encoding Queries Updates Secondary indexes Text indexes Transactions For example, a secondary index can be represented as: idx:status:active → document_id A text index can be represented as: inv:database → posting_list The storage engine does not need to know what status , active , or database means. It only stores ordered bytes. Why this matters This architecture keeps the core engine small and reusable. The engine focuses on difficult low-level concerns: Durability Page management Transactions Recovery Ordering Concurrency Range scans The DBMS focuses on application-level semantics. This also makes it possible to build different data models over the same engine in the future. A document database is therefore not one giant component. It is a collection of carefully separated layers. That separation is one of the most important architectural decisions inside LioranDB. LioranDB is being developed by Swaraj Puppalwar under Lioran Group . Learn more: LioranDB Lioran Developer Solutions Lioran Group

Every frontend developer eventually reaches the same point. You know React. You know TypeScript. You know how to build components. But then you join a real project. Suddenly the questions are no longer about writing a component — they’re about engineering. How should the project be structured? Where should authentication logic live? When is React Context enough? When should React Query own the data? How do you prevent a codebase from becoming impossible to maintain? What makes a frontend application scalable? How do AI coding tools fit into modern development? These are the questions I kept asking myself while working on frontend applications. The problem wasn’t the lack of information. The problem was that the information was scattered across hundreds of blog posts, GitHub repositories, conference talks, documentation pages, and personal notes. Tutorials Teach Frameworks Modern tutorials are excellent at teaching frameworks. You can easily learn: React Vue Angular Next.js TypeScript But very few resources explain what happens after that. How do experienced teams actually build production frontend applications? How do they organize folders? How do they write maintainable code? How do they review pull requests? How do they optimize performance? How do they scale applications from one developer to twenty? Those are engineering problems — not framework problems. Frontend Engineering Is a Different Skill Writing React code doesn’t automatically make someone a frontend engineer. Frontend engineering includes topics such as: Project architecture Feature-based organization Authentication and authorization API design State management Data fetching strategies Performance optimization Accessibility Error handling Testing CI/CD Monitoring Code quality Documentation Team conventions These subjects rarely live in one place. AI Has Changed the Way We Build Software Another reason I started this project is the rise of AI coding assistants. Learn about Medium’s values Today many de

I want to tell you about a tool I've been using for a few months now, that I can't believe that I ever went without. Recently, I've been more productive, experimental and I've learned more about my development environment just from using this tool. Alright, I'm done burying the lead. The tool is git worktrees . Worktrees is a feature built into git that allows you to have multiple working trees of a repository at once. That means you can have multiple branches checked out, with incomplete changes on each one. Instead of having one repository where your manipulation happens, you can have infinite copies (or, as many copies as you can hold on a hard drive)! Often, I am in the middle of working on a feature and I get an email about an urgent bug or a small change that needs made immediately. Before, I would have to either create a "wip:" commit or stash my changes, and I don't really like doing either one of them. Now, as long as my files are saved, I can safely close my editor, create a new worktree and leave all of my uncomitted changes waiting for me to return. How to set up your project for git worktrees In a normal project, your directory might look something like this: MyProject/ ├── .git/ <-- Git metadata in the project dir ├── bin/ ├── obj/ └── Program.cs After following just a few steps, our projects will look more like this: MyProject/ ├── .git/ <-- Git metadata ├── feature-x/ <-- Worktree #1 ├── bin/ ├── obj/ └── Program.cs └── bugfix/ <-- Worktree #2 ├── bin/ ├── obj/ └── Program.cs A full copy of the project's files. In order to set a project up this way, it's best to start in an empty directory, with your project hosted on a remote git server. First, create a directory for your project. mkdir MyProject && cd MyProject Once inside, we're going to clone a bare repository. A bare repository doesn't contain a working tree or any of the files of the project, just the git metadata. We pull that and put it in the .git directory by running the following command.

The different IMAX filming and projection formats have a dramatic impact on the theater viewing experience.

After selling a barely disguised version of the hit DJI Osmo Pocket 3 in the United States last year, Xtra Technology seemed ready to sneak in its version of the new dual-lens Osmo Pocket 4 Pro, too. But today, Xtra has abruptly removed the new Xtra Muse 2 Pro from its website landing page, is […]

I'm a marketer. When I land on a developer tool's website, I don't open the pricing page. I don't read the features. I don't watch the demo. I ask one question: "Can I explain what this product does in 10 seconds?" If the answer is no, you've already lost me. I've worked with enough SaaS products to know that most of them don't have a product problem. They have a communication problem. Developers spend weeks building a feature. Marketing spends days trying to explain it. Users spend three seconds deciding whether it's worth their time. That's a brutal mismatch. The best products I've seen don't try to sound intelligent. They try to sound obvious. You read the headline and instantly think, "I know exactly who this is for." That's incredibly hard to achieve. And it's usually the result of dozens of conversations between product, engineering, support, and marketing. So here's my hot take: A feature isn't finished when it's merged into main . It's finished when a complete stranger understands why it exists. As a marketer, that's the lesson building products has taught me. Curious to hear from developers: Have you ever built something technically impressive that users simply... didn't understand?

In response to a report that Nothing is planning to "exit 12 markets as global shipments decline," Nothing cofounder Akis Evangelidis said the company is "reorganizing" and laying off some of its staff, but that "the reported numbers are way overblown." Evangelidis disputed a claim about underwhelming sales for Nothing's Phone 4B, saying it "sold […]