标签：#c

Why we built Veridexa Many document analysis workflows still rely primarily on OCR OCR is useful for extracting text, but it cannot answer one important question: Does this document show signs of fraud or manipulation? That question led us to build Veridexa. The problem Organizations receive thousands of digital documents every day: Passports National IDs Academic certificates Bank statements Employment documents Invoices Reading the text is only one part of the process. The difficult part is detecting manipulation, inconsistencies, forgery, or suspicious evidence before making a decision. Our approach Instead of relying on OCR alone, Veridexa combines multiple evidence sources into a single fraud assessment. The platform analyzes: OCR extraction Metadata Image forensics Security features Document structure Cross-evidence consistency The result is an explainable decision instead of a simple confidence score. Explainable decisions Every analysis ends with one of three outcomes: ACCEPT MANUAL REVIEW REJECT Each decision is accompanied by supporting evidence so reviewers understand why the system reached that conclusion. Public benchmark We also believe AI systems should be transparent. For that reason Veridexa publishes a public benchmark together with methodology and performance reporting instead of asking users to trust marketing claims. API-first Try the public demo, explore the benchmark, or integrate the API. Feedback from developers and security professionals is always welcome. https://veridexa.io Developers can integrate Veridexa into their own applications using our API while organizations can use the web platform without writing code. We'd love your feedback We're continuing to improve the platform and would genuinely appreciate feedback from the developer community. Website: https://veridexa.io

It's Monday morning. You open your laptop. Coffee sits beside you. LinkedIn is open in one tab. A job portal is open in another. Your inbox is empty. Before lunch, you've already applied for five jobs. Tomorrow, you'll probably do it all again. This is why people say, "Finding a job is a full-time job." But once you're living it, you realize it isn't just a saying—it's a reality. Unlike a regular job, there's no salary, no weekends off, no annual leave, and no guarantee that today's effort will lead to tomorrow's opportunity. More Than Just Clicking "Apply" From the outside, job hunting looks simple. Upload a resume. Click Apply . Repeat. In reality, every application is a small project. The resume is tailored to match the job description. A cover letter is rewritten. LinkedIn is updated. The company is researched. Interview questions are reviewed. Sometimes a portfolio is improved before clicking Submit . What looks like a two-minute application often takes thirty minutes—or more. Multiply that by dozens of applications, and job hunting quickly becomes a full-time commitment. The Numbers Nobody Sees Fifty applications. Ten automated acknowledgements. Three interview invitations. One final-round rejection. And then... The cycle begins again. Many applications never receive a response. Some positions are filled internally. Others are paused, redefined, or quietly removed. From the candidate's perspective, every unanswered application feels the same—another day without clarity. The system doesn't tell you if you were close. It only tells you if you made it. _ Waiting Becomes Part of the Process After clicking Apply , another phase begins. Waiting becomes part of the routine. Refreshing email. Checking LinkedIn. Looking for missed calls. Hoping today's notification finally brings good news. A rejection can be disappointing. But uncertainty is often harder. At least a rejection provides an answer. Uncertainty leaves people creating their own. The Work Doesn't Stop After

Your Contact Form 7 form can work perfectly from a user's perspective and still fail to deliver a lead to your CRM. The visitor fills out the form. The browser shows a success message. The WordPress form appears to have submitted correctly. But when you open Systeme CRM, the contact is nowhere to be found. This is one of the most confusing problems in form-to-CRM integrations because a successful form submission does not necessarily mean a successful API request. The complete workflow has multiple stages: Visitor ↓ Contact Form 7 ↓ WordPress ↓ API Request ↓ Systeme CRM ↓ Contact Record ↓ CRM Automation A failure at any stage can break the workflow. The key to debugging the integration is to stop treating the form submission as a single event and start checking each stage separately. First, separate the two different types of success There are two different questions: Did Contact Form 7 submit the form? This is a WordPress-side question. Did Systeme CRM accept and process the API request? This is an API and CRM-side question. These are not the same thing. A form can successfully collect: Name: John Doe Email: john@example.com Company: Example Inc. while the API request fails because: the endpoint is incorrect authentication is missing the request method is wrong the JSON payload is invalid the CRM expects different field names a required field is missing The first debugging step is therefore to identify exactly where the data flow stops. Step 1: Confirm that Contact Form 7 is collecting the expected data Start at the beginning. Look at the form fields: [text* your-name] [email* your-email] [tel your-phone] [text company] [textarea your-message] The important values are the actual field names: your-name your-email your-phone company your-message A common mistake is to assume that the visible label is the field name. For example: Visible label: Full Name Field name: your-name The integration needs the submitted field value associated with the actual field name. Before

Your agent isn't hallucinating. It's reasoning correctly over the wrong inputs. Here's a failure pattern every team running agents in production has hit: An alert fires. The agent investigates: pulls metrics, checks recent deploys, scans logs, proposes a fix. The fix is confidently, articulately wrong. Instinct says blame the model (bad reasoning, needs a better prompt, maybe a bigger model). Then someone reconstructs what the agent actually saw. The metrics query returned a 5-minute-old cached aggregate. The deploy list was fetched before the relevant deploy landed. The log window was truncated at 1,000 lines and the line that mattered was #1,014. Given those inputs, the agent's conclusion was reasonable. It just wasn't debugging the incident that happened. It's garbage in, garbage out, with a twist that makes it worse for agents than for any previous software. A dashboard shows you the garbage. A human sees a stale chart and might notice the timestamp. An agent consumes the garbage silently and acts on it, with fluent reasoning layered on top. The output doesn't look like garbage; it looks like a confident, well-argued investigation. That confidence is what makes it dangerous. Why is this surfacing now For chatbots, context was mostly a retrieval problem over documents; mediocre retrieval meant a mediocre answer a human would shrug at. Three things changed with production agents: Agents act. A stale metric doesn't produce an off paragraph; it restarts the wrong service or pages the wrong team at 3AM. The cost went from cosmetic to operational. The input surface exploded. A production agent reads metrics, logs, deploy history, tickets, and chat from separate systems, each with its own latency, rate limits, caching, and clock. The "world" it reasons over is stitched together from partial snapshots, inside the model's reasoning loop, where nobody can inspect it. Errors compound. A single wrong input gives a slightly wrong answer. A 20-step investigation where step 3'

Hugging Face is urging users to rotate any access tokens stored on the platform and review account activity.

Back in July my scheduled DEV.to publishing run failed at the very first step — the quota check couldn't reach dev.to:443 at all. Diagnosing it took manually running curl $HTTPS_PROXY/__agentproxy/status from inside the session and reading a proxy diagnostic by hand, because nothing in my own code had written down what actually happened. Not which host, not which of my two HTTP helper functions made the call, not a timestamp, nothing. The failure was real and the fix (get dev.to added to the environment's egress allowlist) was correct, but I found it by treating my own server as a black box and probing it from outside, which is exactly backwards for code I wrote myself. eight tools, one shared blind spot server.py is an 8-tool FastMCP server: three GitHub tools, four DEV.to tools, one that shells out to claude -p . Every HTTP-calling tool routes through one of two helpers: def _gh ( path , method = " GET " , data = None ): req = urllib . request . Request ( f " https://api.github.com { path } " , method = method ) req . add_header ( " Authorization " , f " token { os . environ [ ' GITHUB_TOKEN ' ] } " ) req . add_header ( " Accept " , " application/vnd.github.v3+json " ) if data : req . add_header ( " Content-Type " , " application/json " ) req . data = json . dumps ( data ). encode () with urllib . request . urlopen ( req ) as r : return json . loads ( r . read ()) def _dev ( path , method = " GET " , data = None ): req = urllib . request . Request ( f " https://dev.to/api { path } " , method = method ) req . add_header ( " api-key " , os . environ [ " DEV_TO_API " ]) req . add_header ( " Content-Type " , " application/json " ) req . add_header ( " User-Agent " , " developer-presence-mcp/1.0 " ) if data : req . data = json . dumps ( data ). encode () with urllib . request . urlopen ( req ) as r : return json . loads ( r . read ()) Neither one logs anything. When urlopen raises, the caller — whichever @mcp.tool() function invoked it — sees a bare urllib.error.HTTPEr

The original concept behind this tool won the runner-up award for the Github CLI Challenge earlier this year. As a reminder, ReflectCLI is a tool to promote thoughtful coding through structured reflection. What does it do? Before each commit, answer reflective questions to build your personal developer knowledge dataset. The tool is local-first by design. It stores everything locally inside the current Git repo: .git/git-reflect/log.json No account, no cloud sync, no authentication, and no external AI API call. Why build it? AI-assisted development is powerful, but it can encourage cognitive offloading, letting tools do the thinking instead of developing deeper understanding. git-reflect interrupts this pattern by making reflection part of your workflow. Each answer becomes part of your personal knowledge base, documenting not just what you built, but why you built it that way and what you learned. Over time, this dataset reveals patterns in your thinking and helps you grow as a developer. If you want to read about the original tool, check out my previous post here . Since building the original version, I've spent months researching how AI changes the way developers learn, reason, and make technical decisions. Many of the ideas from those discussions have now been incorporated into ReflectionCLI 2.0. What started as a simple Git pre-commit reflection hook has evolved into a local-first thinking tool for developers working alongside AI assistants. What's New Comprehension debt tracking You can now record artifacts you shipped but don't fully understand yet. reflection debt add "Understand why the cache invalidates on user updates" \ --project api \ --tags cache,ai \ --context "AI generated most of the invalidation logic" These artifacts can easily be retrieved and resolved later. reflection debt list reflection debt resolve debt-001 Explain-back mode This workflow focuses on active recall and asks a series of questions to assess your ability to explain the piece of c

Perplexity added "Skills" to its Agent API, letting developers compose built-in and custom skills for more complex agent outputs. More skills mean more actions, and more actions mean more ways to produce an irreversible change. Before you add a fifth skill to your agent, make sure the first four have an undo path. The undo problem An agent skill that writes, sends, publishes, or deploys is an irreversible action if there is no rollback. The more skills an agent has, the more irreversible actions it can take in a single session. A chain of skills (research → draft → format → publish) can complete before a human notices the first step was wrong. The undo contract Every agent skill should declare: { "skill_name" : "publish_to_blog" , "action_type" : "write" , "reversible" : true , "undo_method" : "set_published_false" , "undo_timeout_seconds" : 3600 , "undo_side_effects" : [ "SEO index will retain the URL for up to 24h" ] } If reversible is false , the skill should require explicit human approval before execution. A skill registry with undo support # skill_registry.py """ Registers agent skills with undo metadata. Blocks irreversible skills from running without explicit approval. """ from dataclasses import dataclass from typing import Optional , Callable import json @dataclass class Skill : name : str action_type : str # "read", "write", "send", "deploy" reversible : bool undo_fn : Optional [ Callable ] = None undo_timeout_seconds : int = 3600 side_effects : str = "" requires_approval : bool = False class SkillRegistry : def __init__ ( self ): self . skills = {} self . execution_log = [] def register ( self , skill : Skill ): # Irreversible write/send/deploy skills require approval if not skill . reversible and skill . action_type in ( " write " , " send " , " deploy " ): skill . requires_approval = True self . skills [ skill . name ] = skill def execute ( self , skill_name : str , inputs : dict , approved : bool = False ): skill = self . skills . get ( skill_name ) i

Visual Studio 2026's July update added organization-level custom agents for GitHub Copilot. An organization owner can now define a custom agent that every repository in the org automatically detects and offers in the agent selector. This is powerful and introduces a rollout risk: a single agent definition affects every developer in the organization simultaneously. The rollout problem When an org-level agent is published, every developer working in any repo in that org sees it in their agent selector. If the agent definition contains an incorrect instruction, a broken tool reference, or an overly permissive scope, it affects all developers at once. There is no canary by default. The agent definition review checklist Before publishing an org-level custom agent, review its definition against this checklist. 1. Instruction review # agent-definition.yml (example structure) name : org-code-reviewer description : Reviews PRs for security and style instructions : | Review each file change. Flag: - SQL injection in string concatenation - Missing input validation on public endpoints - Hardcoded credentials Do not modify files. Only comment. tools : - read_file - search_code - post_comment scope : organization Check: [ ] Instructions are specific enough to be testable ("flag SQL injection in string concatenation" not "review for security") [ ] Instructions do not conflict with existing repository-level AGENTS.md files [ ] The agent does not claim capabilities it does not have (e.g., "run tests" when no tools entry supports it) 2. Tool surface audit # Extract all tool references from the agent definition rg -n 'tools:' agent-definition.yml -A 20 | grep '^\s*-' For each tool: What resource does it access? (filesystem, network, repository API) Is it read-only or read-write? Does it require elevated permissions beyond the developer's own role? A post_comment tool can create noise across every PR. A write_file tool can modify code in every repo. Audit accordingly. 3. Canary test Be

I've just published a small npm package called @modyra/core that came out of a very specific pain: handling "slightly complex" app state in frontend projects without ending up with a mess of context, custom hooks and scattered stores. The focus of the package is: Minimal API, all in TypeScript, no hidden magic No heavy runtime: just tools to model the core of your app as a set of state modules Designed to plug into React/Angular/vanilla JS without forcing you to rewrite everything The idea is to treat your app's domain as a set of "core units" with clear responsibilities: each unit exposes state, actions and rules, and the rest of the app simply consumes them. It came out of a few projects where Redux/Zustand/signals etc. were fine, but started to feel either too verbose or not very close to the actual business domain. If you feel like taking a look and tearing it apart, feedback (including harsh ones) is very welcome: naming, API design, examples - everything is still fresh enough to change. And if you think the approach is worth exploring, a star on GitHub would really help me keep pushing the project forward.

For the first time since 2024, StrictlyVC is coming back to New York City — and we're bringing the kind of access you’d expect from an under-wraps event to the whole startup, VC, and dealmaking community.

Read the Tape gives players the same 5 S&P500 stock charts per day to predict. You select low, medium or high confidence and then call the chart UP or DOWN. It's a 1d chart which then resolves over 5 days. Alpha is scored against the Monkey Index, a basket of 11 random coin flips at low confidence which provides a tangible win/lose condition. We're two weeks in and some interesting data is being kicked up. Players like to call tops even though stonks go up- 60% of the 70 charts so far resolve hi

Max Korbacher explains why successful internal development platforms cannot be built on tech alone. He discusses the pitfalls of infrastructure-first thinking, the importance of a clear product mindset, and how to measure real value using DevEx and SPACE metrics. Learn how to align your team, manage tech debt, and foster a thriving community to ensure lasting platform adoption. By Max Körbächer