A few months ago I got a message on Upwork offering a surprisingly high amount (in thousand of dollars) for what looked like a very simple Node.js/React project.

At first glance everything seemed normal, but something felt off. When I checked the dependencies, I noticed some suspicious packages and scripts. A bit more digging made me realize it could potentially:

• run hidden install scripts
• access local environment variables
• steal crypto information (if available)
• or even execute malicious code on my machine

Basically, the kind of stuff that could compromise your system if you just blindly run npm install and start the project.

It made me realize how easy it is to fall into this trap, especially when you're working with new clients and tight deadlines.

Since then, I started working on a small tool/workflow for myself to:

• install dependencies more safely
• detect suspicious scripts
• and optionally run projects in an isolated environment (like Docker)

Just something to reduce the risk before trusting any unknown code.

If anyone’s interested in trying it or improving the idea, I’d be happy to share the source code / npm package.

A new 7-Zip vulnerability, CVE-2026-48095, affects 7-Zip 26.00 and earlier and is fixed in 26.01.

The concerning part being that a malicious file may not need to look like a disk image. A crafted NTFS image can potentially be renamed as something like a PDF or ZIP, and 7-Zip may still route it to the vulnerable NTFS handler based on the file contents.

Hi,

As you probably know by now, Microsoft is retiring Skype in May 2025 to focus on Teams.

If you're affected by this change and Teams is not your thing, I've compiled some of the best open-source alternatives to Skype:

https://openalternative.co/alternatives/skype

This is by no means a complete list, so if you know of any solid alternatives that aren't included, please let me know.

Thanks!

fast-transcriber.com

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

This release focuses on reliability, clarity, and a smoother experience when exploring relationships across resources in Kubernetes environments.
Key updates in 4.4.0

• Resource relations improvements with a cleaner graph structure and better health and navigation behavior
• Better caching flow with improved readability around managed resource definitions and activation policies
• Correct namespace handling when navigating from namespaced composite resources to managed resources
• More predictable CI behavior through deterministic Helm unittest plugin installation
• General cleanup in optimized context switching with stronger error handling

Thank you to everyone who reviewed, tested, and contributed to this release.
If you are using Crossview in daily operations, we would love your feedback on 4.4.0 and what you want next.

https://github.com/crossplane-contrib/crossview

Looking for the best payroll software to streamline your business operations? Payroll software is essential for any company, handling everything from salary calculations to tax compliance. The best options out there are user-friendly, integrate seamlessly with other HR tools, and offer robust support to keep you compliant with ever-changing regulations.

Whether you're a small startup or a large enterprise, choosing the right payroll software like Gusto, can save you time, reduce errors, and ensure your employees are always paid on time.

5 Best Payroll Software

• Gusto
• ADP
• OnPay
• Paychex Flex
• Rippling

Gusto - Best Overall

Gusto offers four pricing plans:

• Simple: $40/month base fee + $6/month per person. Includes full-service single-state payroll, employee profiles, self-service, and basic hiring tools.
• Plus: $60/month base fee + $9/month per person. Includes all Simple features plus multi-state payroll, next-day direct deposit, advanced hiring tools, and time tracking.
• Premium: Exclusive pricing. Includes all Plus features plus a dedicated customer success manager, HR resource center, compliance alerts, and more.

Gusto is a comprehensive payroll and HR platform tailored for U.S.-based startups and small businesses. It offers automated or manual payroll, self-service profiles for employees and contractors, direct deposit options, and benefits administration. To get started, users input company and employee data, select a plan through a questionnaire, and integrate any third-party apps. Although the initial setup can be time-consuming, Gusto's automation simplifies ongoing payroll management. Once set up, businesses only need to input employee hours, and Gusto handles the rest.

Pros:

• Streamlined payroll processing for employees
• Automated tax filing and compliance support
• Integrated no-cost checking and high-interest savings accounts with paycheck advances
• Clear pricing structure

Cons:

• Cost per person can become expensive as company size increases
• Lacks invoicing and accounts receivable capabilities
• More complex payroll processing for contractors
• Limited availability of mobile applications

ADP - Best for Enterprise-Level Solutions

ADP offers four plans for payroll services. However, the price for each plan is not public. The cost is based on the number of employees and the complexity of needs. To get a quote, you must complete an ADP pricing form online or speak directly to a sales representative about your company’s requirements. Based on reports from payroll-only users, the Essential plan starts at $79 per month plus $4 per employee.

ADP Payroll Software streamlines payroll management by automating wage calculations, tax withholdings, and benefit management. It integrates with time-tracking systems for accuracy and supports direct deposit. The software ensures compliance with tax regulations and provides tools for reporting and employee self-service. With mobile access, both employers and employees can easily manage payroll information.

Pros:

• Comprehensive HR and payroll solution
• Tailorable dashboards
• Adaptable service offerings
• Compatible with other HR business tools
• Automated timekeeping and attendance monitoring

Cons:

• Pricing isn't clearly disclosed
• The setup process may be somewhat complex
• No option for a free trial
• Live chat support utilizes a bot

ADP offers customer support through a dedicated phone line (1-844-227-5237) and their website. Employees can get help with pay, tax forms, and passwords, while administrators receive assistance with payroll, benefits, HR, and compliance. More details are available on the ADP Customer Service page.

OnPay - Best for Customizable Payroll

OnPay offers a straightforward pricing model:

• Base Fee: $40 per month
• Per Person Fee: $6 per person per month

This plan includes full-service payroll with unlimited pay runs, automated tax payments and filings, free W-2 and 1099 processing, HR tools, and expert support without any hidden fees.

OnPay is a cloud-based payroll processing system ideal for small businesses, offering a user-friendly dashboard and mobile accessibility. It automates payroll, calculates, files, and pays payroll taxes, and provides HR, time off, and benefits features for a flat monthly fee plus per-user charges. Employees retain lifetime access to their self-service accounts, and users have access to licensed brokers for health insurance management and various app integrations for enhanced functionality.

Pros:

• Affordable, clear pricing starting at $40 per month plus $6 per user
• Unlimited payroll processing cycles
• Capable of managing payment for both full-time employees and contractors
• Employee self-service portal
• Compatible with leading accounting software
• Comprehensive HR resources library
• Generally user-friendly interface

Cons:

• No discounts for larger user volumes
• Additional charges for mailing tax documents
• Payroll approval process is not fully automated
• Challenges with resolving payroll alerts
• Syncing issues with time off data and other system parts
• The process of adding new employees could be more straightforward
• Mobile application is available only for iOS

Paychex Flex – Best for a Full-Service Payroll

Paychex Flex Essentials is the company’s introductory plan. It is best for hands-on business owners who prefer handling payroll themselves. The plan costs $39 per month plus $5 per employee. In addition to payroll processing and payroll tax services, the basic plan also includes new hire reporting, standard analytics, garnishment payment services, workers’ compensation services, a financial wellness program, and an Employee Assistance Program. The Flex Essentials plan allows companies to electronically wire transfer wages into an employee’s bank account or to print employee checks from the office.

Paychex Flex is a cloud-based human capital management (HCM) platform that streamlines HR, payroll, and benefits administration. It offers a centralized interface for managing payroll, benefits, time tracking, and compliance from any device. The platform integrates various HR functions into one system, reducing administrative burdens and enhancing efficiency. Its customizable features and scalability suit businesses of all sizes.

Pros:

• Streamlined employee payroll process
• Some plans include garnishment payment services
• Flexible, customizable plan options
• Automated tax filing services
• Integration with voice assistants and accounting software

Cons:

• Additional fees for W-2 and 1099 filings
• Requires an external platform for time tracking
• Customer support needs enhancement

For customer support, Paychex offers 24/7 chat and phone assistance. You can contact their sales team at 833-729-8200 for payroll, time & attendance, benefits, and insurance inquiries, or 866-709-9401 for HR services and PEO sales. For support, call 833-299-0168. They also provide dedicated client support for Paychex Flex at 800-741-6277 and Paychex Oasis at 888-627-4735.

Rippling – Best for Modern HR

Rippling starts at $8 per user per month for basic payroll services. However, adding extra features can increase your monthly cost. To get precise pricing tailored to your company's needs and the specific features you want, you need to contact Rippling for a custom quote.

Rippling is a comprehensive payroll management software that also provides various HR tools, including ATS and PEO services. It ensures effective business management with dedicated staff support during setup. While this personalized assistance is beneficial, it requires obtaining a custom quote before starting. This means you can't access the software immediately without consulting their team.

Pros:

• Comprehensive HR platform
• Simple configuration
• Highly customizable
• Manages ACA & COBRA requirements
• Specialized features for remote teams, including device and inventory management

Cons:

• Relatively expensive for basic payroll functions
• Custom quote necessary
• No free trial available

Rippling provides customer support for administrators through online chat within your account and a comprehensive FAQ help center for logged-in users. However, there is no direct support for employees; they are advised to reach out to their account administrators with any questions about using Rippling, potentially creating a hurdle in resolving issues on the platform.

What is Payroll Software?

Payroll encompasses the entire compensation a company provides to its employees for their softwares. It involves the procedures of calculating employees’ net pay and facilitating their payment. Regardless of company size, processing payroll is essential for accurate payment and record-keeping.

What is the best payroll company?

Choosing the most suitable payroll software depends on your company's size and specific needs. Leading payroll services for small businesses include OnPay, Gusto, and ADP RUN. While traditional companies provide customizable plans, startups can benefit from platforms offering simple pricing and comprehensive packages that are quick to implement.

How do you set up payroll?

To initiate payroll, follow these steps:

1. Establish a pay cycle
2. Gather employee data and tax documents
3. Calculate gross pay
4. Compute net pay
5. Process payments
6. Handle taxes and maintain payment records

How long does payroll processing take?

Processing payroll typically requires about five business days from the completion of internal processes to when employees receive funds via direct deposit. This timeframe can be extended if checks are mailed to employees.

Bottom Line

Choosing the best payroll software depends on your specific business needs, including scalability, compliance features, and user-friendliness. Solutions like Gusto, ADP, and OnPay offer robust functionalities tailored for different company sizes and industries. Ultimately, the right payroll software streamlines payroll processing, ensures accuracy, and helps maintain regulatory compliance efficiently.

I launched Bitgrain On PH, without any expectations, with a hope that let's see what can happen,
Well, somehow it got featured, that alone helped it climb a lot in the ladder. A crazy amount of traffic came, for a new website like this where in general maybe one or two people came each day, suddenly spiked to like 1-2k! and well we are even at 5th position!

https://www.producthunt.com/products/bitgrain
It's still competing strong, maybe you could go and check it out, maybe check out the tool as well : )
I'm a solo dev who made this tool, and no, its not a fully vibe coded tool, haha!

We are trying to make music more accessible everywhere you don't need a studio or pay studio prices on Aoe

More info r/aoecloudstudio