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Anthropic's popularity with business users is growing so well that the latest beef with the government might actually boost it, data from Ramp suggests.
African payments infrastructure company Flutterwave has hit a new valuation and landed blockchain company Ripple as investor and partner.
Unlike many of his tech industry peers who have cut thousands of jobs citing the need to restructure to make the most of AI, Robinhood's CEO Vlad Tenev conspicuously made no mention of AI in his note about layoffs.
Intro If you work with real-time financial tick data via WebSocket long connections, you’ve probably run into frustrating hidden issues: Connections show as online, but data stops flowing. Adding/removing trading symbols triggers connection storms. Minor network glitches break your data pipeline without obvious error logs. Today I’ll share a practical Python solution built around single-connection dynamic subscription and heartbeat timeout detection . We use alltick api as our example throughout this post. I’ll cover real production pain points, architecture, full runnable code, common bugs and optimization results. All code can be directly used in your projects. Real Production Pain Points & Background My team maintains real-time data pipelines for stocks, forex and cryptocurrencies. A core requirement is to add or remove monitored trading symbols on demand. At first, we created one independent WebSocket connection for each symbol. This simple approach caused a lot of problems in production: Bulk symbol updates create frequent connection creation and destruction, leading to reconnection storms and high network load. Ghost subscriptions: Data keeps coming even after you send an unsubscribe request. False-alive sockets: The connection status stays connected, but tick data stops completely. Your analysis logic runs on invalid data. We refactored the architecture to use one single persistent WebSocket connection for all symbols, plus an independent thread for heartbeat monitoring. After the upgrade, all hidden connection issues are gone, and the system runs stably under high-frequency data traffic. 4 Common WebSocket Issues in Fintech Let’s break down the most frequent problems when using WebSocket for financial streaming. 1. Connection Flood & Reconnection Storms Rebuilding connections every time you update symbols causes endless handshakes and closures. It wastes system resources and makes the entire data stream unstable. 2. Undetectable False-Alive Sockets Network j
JPMorgan can't be pleased by any of this.
What UAE BNPL providers need to verify under CBUAE short-term credit rules, which income sources matter, and how automated income verification fits compliance.
Connecting to a payment gateway rarely fails because of business logic. More often, it fails at the very first technical step: authentication. If you’ve ever worked with payment APIs, you know the drill. Before sending a single request, you need to generate a cryptographic key pair and sign every request correctly. Sounds straightforward—until you actually try to do it. The hidden hurdle in every integration To securely call the Payneteasy API, each request must be signed. That means: Generating an RSA key pair (usually via OpenSSL) Converting between PKCS#1 and PKCS#8 formats Building a correct signature base string Percent-encoding everything properly Signing with RSA-SHA256 or HMAC-SHA1 Assembling the Authorization header One small mistake—a missing character, wrong encoding, or incorrect format—and your request gets rejected. For teams without deep cryptography expertise, this step alone can turn a one-day integration into a week-long debugging session. If you’re curious, the full manual process is documented here: https://doc.payneteasy.com/integration/general_api_usage/request_authentication_methods/oauth.html#generating-key-pair The solution: Key Pair Factory We built the Payneteasy Key Pair Factory to remove this bottleneck entirely. Instead of dealing with OpenSSL commands and key formats, you can generate everything you need in just a few clicks. What it does: Generates a ready-to-use RSA key pair Ensures correct formatting for Payneteasy APIs Eliminates manual conversion and configuration errors Keeps the private key on your side Provides a public key for request verification No cryptography expertise required. The tool is open-source and available on GitHub: https://github.com/payneteasy/key-pair-factory Why this matters This is not just about convenience—it directly impacts integration speed and success. With the Key Pair Factory, you get: Faster onboarding Fewer integration errors Less back-and-forth with support teams A smoother developer experience I
Every AI coding tool can write Python — Cursor, Claude Code, Windsurf. None of them can run it safely in production. That gap between "AI wrote the code" and "the code ran safely" is exactly what I'm building jhansi.io to close. This series documents the journey. One layer of the problem at a time. The execution gap When AI generates code, four things still stand between you and prod: Dependencies — Install the right packages, with versions and licenses you trust Isolation — Run it hard-sandboxed. No host access, no outbound network, no surprises Secrets — Let AI use your API keys without ever letting it see or leak them Audit — Log every execution. Prompt, code, result, timestamp. Compliance-grade. Most teams stop at step 1. Banks and fintechs can't. FCA, SOC2, and the EU AI Act require audit trails for AI actions. You can't eval() your way through an audit. jhansi.io is the missing run() for AI-generated code. Open core, cloud sandbox, built to close each part of the gap — layer by layer. The series Part 1 — Persistent sandboxes Why "ephemeral" breaks debugging, state, and compliance. The case for giving every AI a home directory. → Read Part 1 Part 2 — Dependency management (coming soon) Detecting, installing, and locking deps across Python, Node, Go, and Java. With SBOMs and policy built in. Part 3 — Isolation (coming soon) What "hard isolation" actually means. Containers, Firecracker, zero trust networking, and the metadata service attacks you haven't thought of yet. Part 4 — Secrets (coming soon) Kernel-level proxies. AI can call Stripe without the key ever entering the sandbox. Part 5 — Audit (coming soon) Who ran what, when, with which prompt. Hash-chained logs that satisfy auditors, not just engineers. Building this in public. Follow the series on Dev.to , Linkedin , and X . Code is Apache 2.0 at github.com/jhansi-io .
Applications for Startup Battlefield 200 officially close on June 8, 11:59 p.m. PT. Don't wait any longer. Secure your shot at competing on the Disrupt Stage at TechCrunch Disrupt 2026 this October at San Francisco's Moscone West.
TL;DR Reconciliation is the part of a payments stack nobody architects for on day one and everyone pays for on day 200. The job: prove that every internal transaction matches the acquirer's settlement file, in the right currency, with the right fees, on the right value date — or surface the diff fast. The mechanics: normalize files → land into an events table → project to a read model → diff against the internal read model → buckets for ops to resolve. The boring details (file formats, fee parsing, FX rounding, value dates) are where 90% of the work lives. If you've ever opened a CSV from an acquirer at the end of the month, sorted by amount, and tried to "just match it in Excel" — yes, this post is for you. What "reconciled" actually means A transaction is reconciled when, for the same logical payment, three views agree: What you sent — your internal record of the charge/payout (your read model). What the acquirer says happened — their settlement file or API report. What the bank actually credited / debited — the bank statement. Disagreements are normal. Persistent disagreements are how you lose money slowly and never know. The shape of a settlement file Across the major acquirers, settlement files look broadly similar — and broadly different in the places that matter: Field Variants you'll see Transaction reference acquirer's transaction_id , sometimes plus a merchant_reference round-tripped from you Gross amount minor units / decimal; transaction currency vs settlement currency Fees inline per-row, or aggregated at the file footer, or in a separate fees file FX inline rate vs separate FX file; sometimes only the converted amount Value date when the bank actually moves money — often T+1/T+2 from event date Adjustments refunds, chargebacks, fee corrections, reserves — usually mixed in Encoding UTF-8 if you're lucky; CP1252 / fixed-width / SWIFT MT940 if you're not Granularity one row per transaction or daily aggregates per merchant or both There's no industry-clean
Ramp has nearly tripled its valuation over the past year as investors scramble to grab a part of the fast-growing startup.
I am a full time trader and part time developer based in Karachi, Pakistan. A year ago I sat down to research how to properly compare brokers on the Pakistan Stock Exchange. Three hours later I had 11 browser tabs open, two of which had broken links, one had data from 2019, and none of them had everything I needed in one place. So I built PSX Pulse. What PSX Pulse Is PSX Pulse is a free stock market education platform for Pakistani retail investors. Everything a beginner needs to start investing in Pakistan's stock market — in one place. What is live right now: 35 verified SECP-licensed brokers with full contact details Complete mutual funds directory across 15 AMCs DCA calculator with realistic return scenarios 30-day beginner learning path Islamic investing guide PSX sector guide covering 12 sectors IPO tracker 100-term searchable glossary Weekly market recap every Friday All free. No login required. Live at: https://psxpulse.xwen.com.pk/ The Stack React + Tailwind CSS for the frontend. Vercel for hosting — free tier handles everything comfortably. No backend for most features — localStorage and static data keeps it fast and simple. Newsletter handled via a serverless Vercel function writing to a private GitHub CSV. What I Learned Building This Solo 1. The information gap in emerging markets is enormous Pakistani investors are not underserved because nobody cares. They are underserved because nobody with the technical skills to build tools also has the market knowledge to know what those tools should do. Being both a trader and a developer turned out to be the actual unfair advantage. 2. Free tools beat content for SEO My DCA calculator and broker directory pages get more consistent Google clicks than any article I have written. Tools solve a specific search intent that AI overviews do not replace — people still need to interact with a calculator, not just read about one. 3. Building in public is uncomfortable but worth it Sharing what you are building before it i
The British fintech has built a waitlist of about 450,000 users in India as it prepares for a broader launch.
Most software projects focus on producing outputs. Most AI projects focus on producing predictions. But real organizations don't operate on outputs or predictions alone. They operate on decisions. A decision has consequences. A decision creates risk. A decision consumes resources. A decision changes the future state of a system. Over the last few months, I've been studying and building systems around a simple question: How can we make decisions more explainable, auditable, and repeatable? This led me toward concepts such as: event-driven architectures decision logging risk evaluation pipelines audit trails feedback loops operational intelligence systems Instead of asking: "Can we predict what will happen?" I'm becoming more interested in asking: "Can we explain why a decision was made?" and "Can we reproduce that decision six months later?" Current areas I'm exploring: Financial decision systems Risk infrastructure Event-driven architectures Blockchain compliance workflows Operational intelligence platforms One of the projects I'm currently building is an Event-Driven Decision Logging System (EDDL), designed to explore how organizations can record, audit, and replay critical decisions over time. Still learning. Still building. Still refining my understanding of how complex systems operate under uncertainty. Looking forward to sharing the journey here. systemsdesign #architecture #backend #fintech #softwareengineering #eventdriven #riskmanagement