... apps on one VPS: Coolify and three self-hosted Supabase databases. This week I set up proper backups and, for the first time, tested one by restoring it into an empty database and comparing every table. The copy itself looked fine. The restore didn't: - 164 of 166 tables

... maintain comparison tables inside an existing HTML page with HtmlDrag. Insert a table

... Claude writes the table-based HTML layout -> ChatGPT checks Outlook rendering quirks -> Perplexity verifies dark-mode CSS compatibility HTML emails feel like coding in 1999 tables

... error on screen, just silence where data should've been. I asked my AI tool to fix it twice. Twice it guessed wrong. So I actually opened Vercel's logs myself. And there it was: insert or update on table "score_history" violates foreign key constraint "score_history_user_id_fkey" I didn't know what that sentence meant six months ago. I do now. It meant one of my database tables

... This is an exciting, utility-focused release. We completely rebuilt social saves, added a new table view for your library, expanded AI language support to 62 languages, and improved the AI behind tags, connections, summaries, and quizzes. Don't miss the release notes , which have more detail and information on what's coming next. There is also an incredible customer story and an open AMA with a Recall power user on building a second brain , running through ... ... social support around two weeks ago, but ran into issues where Instagram transcripts or articles were not fully saved. We have now rebuilt how we capture, transcribe, and present social content across Instagram, TikTok, X, LinkedIn, Reddit, and Facebook. Table

... promise was that natural language would replace the query editor. In practice, a lot of these tools produced syntactically perfect SQL against a schema they didn't actually understand, confidently answering the wrong question because they guessed which table "revenue" lived in. Now we're in the agentic phase: systems that plan, retrieve context, run analysis, check their own work, and hand back a narrative answer. A bunch of products sit here now, including ada.im, various "chat with your data ... ... toward 95%. The intelligence wasn't the bottleneck. They break failures into three modes that generalize to basically every tool in the category: Concept-to-entity ambiguity: a vague word like "revenue" or "active users" maps to many plausible tables

... things people kept running into. What's in 1.0.12: Tags: free-form, autocomplete-first note tags with color coding and a filter popover in the library (multi-select, so you can narrow by more than one tag at once). Tables: paste one in and it's preserved instead of turning into plain text, plus a /table command and right-click row/column editing. Emoji: type :fire: and pick from a grid, or use /emoji. No dependency on macOS's Character Viewer ...

... query performance lag $activeLogs = TruckLog::with('driver') ->whereDate('created_at', today()) ->orderBy('created_at', 'desc') ->paginate(15); return view('dashboard.fleet', compact('activeLogs')); } } In the Blade view, I mapped these variables directly to the pre-styled table components inside the Arcone template. The transition of static dummy data to actual database records worked nicely, and the table

... database. Some were from deleted plugins. Some were temporary settings. I first created a backup. Then I used WP-CLI to clean temporary data. wp transient delete --all wp cache flush After that, I checked large database tables. The wp_postmeta table

... years of leftover plugin data. After checking the database, I found thousands of expired transients. I logged into the server and cleaned everything through WP-CLI: wp transient delete --all wp cache flush Then I optimized several large tables directly in MySQL: OPTIMIZE TABLE

... plain English. It writes the code. Here is what I actually use it for. 1. Full account audits in minutes. Feed it raw data. Get a client-ready brief back. 2. Automated weekly client reports. Pulls data, builds tables, emails it I do not touch a thing. 3. Budget pacing and overspend forecasting. Projects end-of-month delivery. Flags blown budgets early. 4. Creative performance breakdowns. Ranks every ad by ROAS, CTR, CPA across both platforms. 5. Cross-channel attribution ... ... sets cannibalizing each other. 7. Search term mining and negative keyword lists. Finds the negatives I should have added last month. 8. Landing page and tracking QA. Checks every URL and pixel fire in the account. 9. Competitor

... this exact problem. It s why we are building Ethos by DocuShell (it's an open-source verifier for document citations, source evidence, and RAG). While building it, we realized just how easily LLMs get totally confused by simple tables or negative sentences (like missing a "not" or "except"). We have our own way of handling it, but we know there isn t just one right answer. I really want to know how other builders and founders here are tackling ... ... workarounds below. Comment from Saumil D(@saumild27): @alpertayfurr  thanks, this is very useful. I agree. Summaries can hide the mistake, but extraction and RAG Q&A make it very clear when the model has stretched the evidence. Tables

... Side note: We're live on Product Hunt today with our Slack Data Agent ! Export to Excel You can now export to Excel (.xlsx) anywhere you could already export data. Charts, tables, markdown tables

... Pressing Escape and clicking the close button now reliably dismiss the search window, which previously could stay open. Tag actions on mobile. The tag actions menu no longer gets stuck after navigating back on mobile. Editor crash on broken tables. Notes containing malformed tables

... current beta is focused on proving the connection, tenant, OAuth, and token-vault architecture before allowing any live customer data flow. What is working now: The Laravel beta app is live in its private beta environment. The users table has been repaired and confirmed working. Core tenant tables

... screenshots, and handwritten notes. What makes it a bit different from generic OCR: Built on PP-OCRv5 + PaddleOCR-VL (2M params) benchmarks competitively against Google Vision and AWS Textract, especially on CJK languages 22 specialized tools handwriting (including cursive), tables, math LaTeX, receipts, screenshots, Arabic / Japanese / Chinese Free tier: 20 pages/day, no signup needed for casual use Pro: $10/mo (annual) for 3,000 pages, batch up to 20 files, .txt/.md/.docx export Supports JPG, PNG, PDF, WebP Use cases we keep ...

... current beta is focused on proving the connection, tenant, OAuth, and token-vault architecture before allowing any live customer data flow. What is working now: The Laravel beta app is live in its private beta environment. The users table has been repaired and confirmed working. Core tenant tables

... Last month, I did something that felt slightly insane. I took our product description, fed it into ChatGPT, and asked it to build a competitor. Not a parody. A real competitor

... Nowhere. What I Thought Mattered (It Didn't) What I Prioritized What AI Actually Cares About Beautiful design Structured content Clever copy Direct answers SEO keywords Complete questions Backlinks Original data Social proof Comparison tables I was optimizing for humans. AI is not human. Lesson #1: AI Doesn't Read It Extracts Humans read your About page. They feel your brand voice. They appreciate the design. AI does none of that. AI scans your page looking for one thing: answers ... ... project management tool for remote design teams. Here's exactly how we work." Feature lists Problem-solution paragraphs Vague benefits Specific outcomes Lesson #2: AI Loves Comparisons Even If You Lose This one hurt. I avoided mentioning competitors

... just exposure, but the degree to which AI treats your brand as a credible source . Share of Voice (SOV): Represents your brand s voice share within AI narratives the probability that AI mentions your brand rather than a competitor when answering a topic-related question. Sentiment: Captures the emotional tone of AI mentions positive, neutral, or negative showing how AI portrays your brand. Together, these four metrics answer the two most fundamental GEO questions: How frequently does AI mention ... ... brand? and In what tone and context is the brand represented? 1.2 Trends and Competitor

... Every AI answer about your category includes comparisons. Always. When someone asks "best CRM for agencies," AI doesn't list one tool. It lists 3-5. And it compares them. The data: Content Type Citation Rate Pages with comparison tables +470% vs

... many found they were invisible. But here's what scared me more: Of those who WERE visible, 43% had incorrect information in AI answers. Not "suboptimal." Not "could be better." Wrong. Outdated pricing. Missing features. Wrong founder bios. Competitors credited for your work. And the average error sticks around for 4 6 months . Today, I'm sharing the full dataset: 1,000+ SaaS products, 75,000+ AI answers, and the hard truth about what's being said about you when ... ... analyzed 50 brands that discovered errors and tracked the impact. Error Type Average Revenue Impact Pricing error (higher than actual) 47,000 Pricing error (lower than actual) 23,000 (leakage) Missing critical feature 38,000 Wrong positioning (enterprise vs

... first 200 words) Answer the question immediately No introduction, no fluff One paragraph max Include the target keyword naturally Part B: The Supporting Data (next 400 words) Add statistics, examples, or proof points Use bullet points or a mini-table Cite sources where possible Part C: The Structured Breakdown (remaining content) Break into H2 sections with clear labels Each section answers a sub-question End with a summary table

... schema 71% 19% Cite data sources 68% 27% Part 3: What content wins AI citations? This is the question everyone asks. Here's the data: Top 5 content types by citation rate: Comparison pages (X vs Y) 3.2x average Structured FAQs 2.8x average Data-driven articles (with stats, tables

... manual research . Going through company websites, product docs, and recent launches. Looking closely at how they price their product. After doing this across ~50 companies, patterns start showing up. API products launching. Teams moving to usage-based pricing. Pricing tables getting messy. Now tools start becoming useful. Apollo helps you reach the right people. Clay helps you scale the signals. But the insight still comes from manual research. Right now the best setup seems ... ... hypothesis, log which ones produced replies (not just opens), and retire the ones that don't. The signals that keep winning are rarely the obvious ones like funding. They're things like a team publicly unhappy with a competitor