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Originally published at nlocoding.com 94% of Fortune 500 companies now contribute to open-source AI projects (GitHub Octoverse, 2026). Not just using them. Actually building the future, brick by brick. Open-source AI isn’t a fringe experiment anymore. It’s the backbone of 2026’s digital economy. The same survey shows 77% of SaaS startups use at least one open-source AI model in production. Power, flexibility, and price—pick all three. Here’s why this trend breaks everything you thought you knew about innovation. Open-source AI dominates enterprise adoption in 2026 Open-source AI platforms are now the default for 62% of enterprises (Gartner, 2026), surpassing proprietary AI for the first time. The data says it: vendor lock-in is dead. Microsoft, Google, and Amazon all run open-source LLMs internally—Meta’s Llama 3 powers 85% of their internal NLP workflows at zero license cost. Why? Transparency. Control. Faster bug fixes. The average company adopting open-source AI saves $1.2M per year on licensing alone (RedMonk, 2026). 62%of enterprises now default to open-source AI (Gartner, 2026) Actionable takeaway: If you’re still stuck on locked-down SaaS AI, run a pilot with open-source alternatives (Llama 3, Mistral, Falcon). Measure cost, speed, and model control. You’ll never look back. 💡 Pro Tip: Pair open-source AI with cloud credits (AWS, GCP) to minimize infra costs in early pilots. Model quality is now open-source’s real advantage The data shows open-source AI models outperform closed models at 73% of NLP benchmarks (Stanford HELM, 2026). This wasn’t true two years ago. Mistral Medium, for example, beats OpenAI’s GPT-4 Turbo at summarization, retrieval, and code generation—free, unrestricted, and running locally. HuggingFace’s leaderboard is led by open models in 18 of 24 tracked domains. You’ll notice something: innovation outpaces regulation. With open weights, anyone can fine-tune or inspect for bias. The top Kaggle winner in 2026 used Falcon 2B, trained on $40 wo

Help wanted: validate a React faceted search SPFx sample in SharePoint Online A new read-only React faceted search sample is ready for the PnP SharePoint Framework webparts repository: Pull request: https://github.com/pnp/sp-dev-fx-webparts/pull/6480 Sample: https://github.com/pnp/sp-dev-fx-webparts/tree/main/samples/react-faceted-search The web part uses SharePoint Search REST ( /_api/search/query ) to search the current site. It supports: Search terms FileType and ContentClass refiners Result counts and metadata Safe encoded query/refiner values Loading, empty, access-denied, throttling, error, and retry states Responsive, accessible Fluent UI rendering The implementation is intentionally read-only and does not use a custom backend or Microsoft Graph. Local verification completed: 4/4 Jest tests passed TypeScript and webpack build passed ESLint passed Production .sppkg packaging passed Gallery metadata validator corrected and rerun successfully Tenant validation is still needed. If you have a SharePoint Online tenant, please test search indexing, result links, refiners, permissions, empty/error states, and narrow web-part widths. Real screenshots and negative findings are welcome; no local screenshot is being presented as tenant evidence. Please share feedback on the pull request. Thank you!

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Help wanted: validate a configurable SharePoint list SPFx sample I have opened a new SharePoint Framework sample for a read-only, configurable list and records browser: Pull request: https://github.com/pnp/sp-dev-fx-webparts/pull/6476 The sample targets common SMB/SME data-view scenarios without reimplementing SharePoint’s editing experience. It uses React 17, Fluent UI v9, PnPjs v4, and SPFx 1.23.2. What the sample does Configures a SharePoint list title and visible internal fields. Uses explicit PnPjs $select / $expand queries and bounded pages. Supports text, number, currency, date, Boolean, choice, hyperlink, and person fields. Provides sorting, bounded paging, optional search, responsive table/card views, safe links, and native item-form links. Includes loading, empty, retry, permission, throttling, generic error, keyboard, and accessible status states. Keeps the MVP read-only: no create, edit, delete, attachments, bulk actions, or custom query language. Help needed Could someone with access to a SharePoint Online tenant please try the sample and report back on: Configuring it against a small ordinary list. Field discovery and supported field rendering. Sorting, search, paging, empty results, and error/retry behavior. Permission handling and narrow-viewport rendering. Keyboard operation and safe item links. One or two representative screenshots for the README. Dummy data is fine. Please remove or blur tenant names, site URLs, user names, record details, and all other sensitive information before sharing screenshots. The code builds and tests locally, but I do not currently have a tenant available for end-to-end validation. Tenant feedback and screenshots would materially help complete the pull request. Thank you to anyone who can spare the time to test it. sharepoint #spfx #opensource #react

Every value in my little embedded key-value store gets encrypted, then its ciphertext gets encoded as a string of A/C/G/T characters before it ever touches the filesystem. Open the file in a text editor and you'll see actual DNA-looking text - not because it's a gimmick, but because that's genuinely the storage format. This is mdc-lite , a ~348KB embeddable encrypted key-value store I built in Rust for places a server can't reach - a watch face, a phone app, a background service. It's part of a larger repo, ModelDB , that also includes MDC, a Python conversational data engine (query AI models, databases, images, and documents in plain English, no SQL) with its own DNA-inspired archival storage tier. The actual storage format Every put() call does this, in order: Pack [key_len][key_bytes][value_bytes] into one plaintext buffer. Encrypt the whole thing with XChaCha20-Poly1305 (a 256-bit key you supply - the crate never generates or stores key material itself; real key custody belongs to the platform's secure hardware, iOS Secure Enclave or Android Keystore). DNA-encode the resulting [nonce][ciphertext][tag] blob: 2 bits per base, 00→A 01→C 10→G 11→T . Every byte maps to exactly 4 bases, so there's no padding ambiguity on decode. Write the ACGT text to disk, atomically (temp file + rename). Filenames are keyed BLAKE3 hashes of the logical key, not the key name itself, so a directory listing alone leaks nothing - no key names, no values, no way to tell how many distinct keys exist versus how many files are on disk. rust pub fn put(&self, key: &str, value: &[u8]) -> Result<(), LiteStoreError> { let mut plaintext = Vec::new(); plaintext.extend_from_slice(&(key.len() as u16).to_le_bytes()); plaintext.extend_from_slice(key.as_bytes()); plaintext.extend_from_slice(value); let nonce = XChaCha20Poly1305::generate_nonce(&mut OsRng); let ciphertext = self.cipher().encrypt(&nonce, plaintext.as_ref())?; let mut record = nonce.to_vec(); record.extend_from_slice(&ciphertext); let ac

Wrap the payment. It runs once across retries, crashes, resumes, and replays. exactly-once is a Python library that makes a side effect run a single time. Wrap the function that pays an invoice or sends an email, or submits a transaction and it executes once per key, then replays its stored result on every later call. Here is the whole integration: from exactly_once import once , Store , current_key store = Store . sqlite ( " effects.db " ) @once ( store , key = lambda inv , ** _ : f " pay: { inv . id } " ) def pay_invoice ( inv ): return payments . transfer ( inv . vendor , inv . amount , idempotency_key = current_key ()) Call pay_invoice(invoice) and it pays the vendor. Call it again from a retry, a resumed run, a replay, or a second worker and it returns the recorded result. The vendor is paid once. The crash it's built for An agent pays an invoice. The transfer reaches the provider and succeeds. The process dies in the moment between the provider's 200 OK and the line that records the result. The agent restarts and reaches the same step again. exactly-once writes a record the instant the agent enters the call. pay_invoice claims the key pay:{invoice.id} , and the store marks it IN_FLIGHT . When the result returns, the store marks it COMMITTED and saves that result. After the crash the record reads IN_FLIGHT with an empty result the library knows a payment started and holds no proof it finished. So it quarantines the key. The agent leaves that payment for a decision and moves on. You give @once a prober that asks the payments API whether a transfer with that idempotency key exists: the library commits the key when the provider confirms the payment, and releases it when the provider confirms none. Until an answer arrives, the held payment stays in the ledger where you can see it: store . list ( state = " in_flight " ) # every payment awaiting a verdict How the guarantee holds Three states, one atomic operation: FRESH ──claim──▶ IN_FLIGHT ──commit──▶ COMMITTED clai

Been looking for a simple, offline ready web application to save things I want to read after Pocket shut down. Couldnt find anything that I liked so created one - hopefully others might like. monkeydust / rightread Read-later: capture links from anywhere, read them clean and offline rightread Capture links from anywhere. Read them clean, later, offline. Paste a link. It gets extracted and it's ready to read, clean and offline. Save a link from your phone's share sheet or your browser toolbar. rightread strips the page down to the article, with no ads, no cookie banners and no newsletter popups, and keeps it readable offline in typography built for long reading. Why this exists On 22 May 2025, Mozilla announced it was winding Pocket down . I'd used it for years for one thing: saving something on my phone and reading it properly later, usually when I was on the tube. The alternatives were mostly 'meh' so I built the small thing I missed. One queue, clean text, works on a plane, running on a server I control with the whole library in a single SQLite file I can copy. The reading list lives on your… View on GitHub

I’m looking for a little community help validating a new SharePoint Framework sample: React Flex Forms . What does it do? The sample contains two SPFx web parts: Form Designer — creates and manages one-page form definitions using supported SharePoint field types. Form Renderer — loads a published form, validates responses, and saves submissions to a SharePoint list. The sample includes automated tests and the local lint, build, and packaging checks are passing. The remaining gap is real-tenant validation and the static screenshots required for the PnP sample README. Could you help? If you have access to a SharePoint Online tenant and a few minutes to spare, please try the sample and capture: The Form Designer working in the SharePoint-hosted workbench. The Form Renderer displaying and submitting a published form. Dummy data is completely fine. Please remove or blur tenant, site, list, user, and other sensitive details before sharing. Feedback about provisioning, permissions, validation, submission, keyboard use, responsive behavior, dark theme, or high-contrast mode would also be very valuable. You can attach the scrubbed screenshots and observations directly to PR #6473 . The setup instructions are included in the sample README. This is a small request, but it would significantly speed up the final validation and help move the contribution toward completion. Thank you to anyone who can lend a tenant or share feedback. sharepoint #spfx #opensource #webdev

I just released Vincent 0.7.0 , and this release marks an important milestone for the project: Vincent now builds Vincent. All development on the project now goes through Vincent workflows — from creating an approved GitHub issue through planning, implementation, verification, human gates, merge, and release preparation. The journey from 0.4.0 to 0.7.0 added quite a bit. Workflows became real interfaces Workflows can declare their expected inputs, including: labels types required fields RE2 validation Vincent also gained a workflow-authoring skill designed around a principle I care about quite a lot: don't use an AI agent when deterministic automation can do the job better. Commands and native control flow come first. Agents are used where reasoning is actually required. Recovery became part of the workflow Real automation fails. So Vincent now has mechanisms for continuing rather than throwing work away: follow-ups on completed tasks recorded repair agents for blocked tasks retry backoff safer daemon backup/restore improved diagnostics through vincent doctor The control plane became scriptable 0.7.0 significantly expands the CLI. Tasks can now be started idempotently, created from GitHub issues, populated through JSON/stdin, queried through vincent status , limited with max_cost_usd , and integrated with notifications. Logs, transcripts, approvals, retries, repairs and task answers can all be handled without entering the TUI. The TUI hasn't been neglected either — tasks now open into a dedicated workspace containing steps, attempts, metadata, output and file-grouped diffs. Vincent builds Vincent This is the part I'm most excited about. My own development workflow now uses Vincent itself: GitHub issue ↓ planning ↓ implementation ↓ documentation ↓ cross-platform verification ↓ human gates ↓ merge ↓ release audit Claude Code, Codex or Cursor can provide the inference. Vincent owns the durable workflow, state and verification around them. That's the architecture I've b