Even one potential bright spot for the GOP could actually be another warning sign, the polling expert said.
Source: Headtopics
FGFTRebCHEYNEYMinM-AM-AO-TAPFPTSAltemus120-31-20-1011Cody121-60-03-5123Ro. Willis201-32-20-0024Curtis271-70-00-2002Hutchinson243-122-30-1029Taylor181-20-21-3032Dubose140-68-92-5218Hill134-101-11-40210Acker120-11-21-4021Lindsay124-110-20-10110Donald110-20-00-0010Hackett-Pray90-20-00-1010Dunkley80-10-00-1010Ludvig40-00-21-1010Rivera41-10-00-0002Totals20016-6715-259-2932052
Source: Hastingstribune
Brooklyn Marie performed Elton John's "Goodbye Yellow Brick Road" during Monday's Cut Week episode. Voting is only open until 2 a.m. Nov. 3.
Source: Grandforksherald
Building Anti-Detect Web Automation & Headless Fleets in Pure Python: CDP Injection, Coherent Fingerprints & SQLite Sessions Web scraping and browser automation in 2026 have shifted from basic HTTP parsing to an adversarial battle against machine-learning anti-bot systems. Platforms like Cloudflare Bot Management, Datadome, Akamai, and HUMAN (PerimeterX) analyze hundreds of browser runtime integrity signals, WebGL hardware pipelines, and behavioral telemetry. In this guide, we break down the architecture of StealthFleet Pro—a production-grade, zero-subscription Python framework for running undetectable browser automation and headless crawling fleets. 1. Why Naive Randomization Fails (The Coherence Rule) A common mistake in web scraping is indiscriminately randomizing browser properties on every request. Novice scripts often pair a Windows User-Agent with an Apple M-series WebGL renderer, or randomize navigator.hardwareConcurrency between 2 and 64 cores across paginations. Anti-bot neural networks immediately flag incoherent fingerprints: [Incoherent Fingerprint Detected] User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) ... Platform: Win32 WebGL Vendor: Apple Inc. ❌ [Anomaly: Apple GPU on Windows OS] Screen: 1920x1080 Color Depth: 30-bit ❌ [Anomaly: Non-standard HDR depth on standard 1080p profile] Result: Immediate Cloudflare Turnstile Challenge To remain undetected, fingerprints must be statistically coherent and stable per session profile. 2. Generating Coherent Hardware Profiles in Python In stealthfleet_pro, we model realistic hardware distributions. When a Windows desktop profile is requested, our generator ensures that screen resolution, WebGL vendor (Google Inc. (NVIDIA) or Google Inc. (Intel)), Direct3D11 ANGLE renderers, hardware concurrency, and client hints strictly correlate: import random from typing import Optional from pydantic import BaseModel, Field class FingerprintProfile(BaseModel): platform: str user_agent: str viewport_width: int = 1920 viewport_height: int = 1080 hardware_concurrency: int = 8 device_memory_gb: int = 16 languages: list[str] = Field(default_factory=lambda: ["en-US", "en"]) webgl_vendor: str = "Google Inc. (NVIDIA)" webgl_renderer: str = "ANGLE (NVIDIA, NVIDIA GeForce RTX 4070 Direct3D11 vs_5_0 ps_5_0, D3D11)" canvas_noise_seed: float = 0.12345 3. Chrome DevTools Protocol (CDP) Pre-Document Injection Injecting evasion scripts via DOMContentLoaded executes too late—anti-bot scripts execute at the root of the HTML document. Using CDP's Page.addScriptToEvaluateOnNewDocument, our overrides run before any site scripts evaluate: async def attach_stealth_pipeline(page, fingerprint: FingerprintProfile): stealth_js = f""" """ cdp_session = await page.context.new_cdp_session(page) await cdp_session.send("Page.addScriptToEvaluateOnNewDocument", {"source": stealth_js}) 4. Serverless SQLite Session Store (Eliminating Repetitive Logins) Logging in on every scrape execution triggers rate limits and SMS verifications. Instead, persistent cookies, localStorage tokens, and warmed session states are stored in a local SQLite lakehouse: import sqlite3 import json class SessionStore: def __init__(self, db_path="sessions.db"): self.conn.execute(""" CREATE TABLE IF NOT EXISTS sessions ( session_id TEXT PRIMARY KEY, domain TEXT NOT NULL, fingerprint_json TEXT NOT NULL, cookies_json TEXT NOT NULL, last_used_at REAL NOT NULL ) """) def persist(self, session_id, domain, fp, cookies): INSERT INTO sessions VALUES (?, ?, ?, ?, strftime('%s', 'now')) ON CONFLICT(session_id) DO UPDATE SET cookies_json=excluded.cookies_json, last_used_at=excluded.last_used_at """, (session_id, domain, fp.model_dump_json(), json.dumps(cookies))) 5. Async Concurrency & Exponential Backoff Unthrottled browser spawns quickly crash cloud VMs. An async fleet manager bounds concurrent processes with asyncio. Semaphore and enforces exponential backoff upon encountering HTTP 429 or CAPTCHA challenges: import asyncio class AsyncFleetManager: def __init__(self, max_concurrency: int = 5): async def execute_task(self, url: str, max_retries: int = 3): async with self.semaphore: retries = 0 while retries <= max_retries: try: # Execute stealth scrape return await self._scrape_with_retry(url) except Exception as ex: retries += 1 if retries > max_retries: raise ex backoff = 0.5 * (2 ** (retries - 1)) await asyncio.sleep(backoff) Conclusion & Complete Codebase Building resilient, autonomous web automation fleets in Python requires no expensive commercial scraping APIs. By understanding the browser runtime integrity checks and managing coherent fingerprints with session persistence, solo engineers can extract data at massive scale on $0 cloud infrastructure. The complete commercial codebase (stealthfleet_pro) and the full 150-page guide are available as part of The Sovereign Software Architect Master Collection: 👉 Download the Full Codebase & Master Collection: https://antigone16.gumroad.com/l/sovereign-architect-quad-vault (Use coupon code **LAUNCH20* for 20% off storewide).*
Source: Dev Community
Check your tickets for Delaware daily lottery draw games here.
Source: The News Journal
Originally published on robatdasorvi.com To quarantine an AI agent after repeated guardrail blocks, count guardrail violations (every action a guardrail blocked) per agent in a rolling window. When the count crosses a threshold, hard-exit the process and write a sentinel file that only a human can remove. Restarts, self-clearing timers, and alert-only checks all fail that test. The lockout check I built wasn't the bug. The bug was assuming a detector could also act as the stop. What Happened When the Lockout Check Returned True? X suspended the account behind my approval-gated growth agent on 2026-07-21. The lockout check fired and returned True, but the caller treated that as a reason to skip one action rather than to stop, and the loop kept running for 3.5 more days. Every outbound action went through a Telegram approval that I tapped by hand, so the approval gate did its job. What failed was the layer above it, the one that was supposed to stop the agent from running at all. Approval limits what gets published. It doesn't stop the process from trying again and again. Why Didn't Exiting the Process Stop It? A plain exit stops one process. A supervisor that restarts processes brings it right back. A plain process exit isn't enough when a supervisor is watching: the agent ran under launchd with KeepAlive, which respawns a process that exits. A stop the supervisor can reverse is just a pause with a delay. The fix needed two parts. The process exits, and a sentinel file records the quarantine. Only a human deleting that sentinel can let the process run again. Why Did the Next Suspension Go Unnoticed? X suspended the account again on 2026-09-22, one week after the reply cap was raised from 16 to 150. This time the lockout check matched none of its known selectors and never fired. I stopped the bot by hand. The lesson is structural. A quarantine built on an external signal inherits every blind spot of that signal, and the platform decides what the signal looks like, not you. If your quarantine depends on a page layout, it will break the day the layout changes. It will break quietly too, because "no match" looks exactly like "no problem." Which Signal Should Trigger the Quarantine? Count the agent's own guardrail blocks and its wasted actions in a window. External detectors and exception counts each miss a whole class of failure. Trigger Fires when What it misses Exception count An action raises an error Silent no-ops: four dead target handles absorbed 88% of comment attempts for about six weeks, and every run logged success External detector The platform shows a known signal Anything the detector has no selector for; it never fired on the second suspension Guardrail blocks per agent per window The agent repeatedly attempts forbidden actions Violations that no guardrail was written to catch Wasted actions An action's target fails to resolve Needs a resolution check on every target An exception-count trigger would have stayed silent the whole time. A wasted-action trigger, counting attempts whose target never resolved, should have fired on the first day, because every one of those attempts pointed at an account that no longer existed. A trigger is only as good as what it measures. The Telegram Skip button silently did nothing for months, so every skipped item got counted as a timeout and the approval stats were wrong. A threshold tuned on those numbers would have been tuned on garbage. How Do You Build the Quarantine So It Holds? My own fix paired the lockout check with a process exit and a sentinel file only a human can clear. The counter below applies the same mechanism to the agent's own blocked actions. Write the sentinel from the block counter, and check it at startup before any other work. The process refuses to run while the sentinel exists. import json, os, sys, time from collections import defaultdict, deque SENTINEL = "/var/lib/agent/QUARANTINED.json" WINDOW_S = 3600 MAX_BLOCKS = 3 recent_blocks: dict[str, deque] = defaultdict(deque) def startup_check(): # Runs before any work. A supervisor respawn exits here, doing nothing. if os.path.exists(SENTINEL): print(f"quarantined: {SENTINEL} present; remove it manually", file=sys.stderr) def record_guardrail_block(agent_id: str, rule: str, queue_depth: int) -> None: now = time.time() q = recent_blocks[agent_id] while q and now - q[0] > WINDOW_S: if len(q) >= MAX_BLOCKS: with open(SENTINEL, "w") as f: If the agent's own OS user writes the sentinel, the agent or any shell tool it can call can delete it. For human-only clearing, the agent's tool sandbox must not be able to write the sentinel directory, or the sentinel should be written by a separate supervisor process running as a different user. The block counter lives in memory, so a restart resets it to zero. In production, persist the counts or keep them in the supervisor, or an agent that gets respawned never reaches the threshold. The startup check is what makes the supervisor harmless. When launchd respawns the process, the new one exits immediately. The respawn still happens, every 10 seconds by default under KeepAlive, but each new process exits before doing any work. To stop the relaunches too, set KeepAlive to a dictionary with SuccessfulExit or PathState instead of true. The sentinel stays until a human deletes it. Notice what the sketch leaves out. It doesn't retry, doesn't sleep and wait, and doesn't clear itself. It does record the queue depth, because whoever clears the sentinel needs to know what's waiting behind it. Clearing the sentinel is the same problem that Break-Glass Access for AI Agents: Approved, Timed, Expiring covers: an elevated action needs a human approval and an expiry. A quarantine the agent can lift isn't a quarantine. Doesn't Human-Only Clearing Make the Agent Useless? Yes, it costs throughput. That cost is smaller than running an agent you can't stop. Gartner forecast in June 2025 that more than 40% of agentic AI projects will be canceled by the end of 2027. Its reasons include escalating costs, unclear business value, and inadequate risk controls, as Gartner's press release reports. The objection is fair. With a human-cleared quarantine, the agent sits idle until someone reads the record, and idle time costs money. I'll take that trade. The alternative is what I had: a loop that kept running after its own detector had flagged the lockout. My inference, not Gartner's: a stop you can't enforce is exactly the kind of missing risk control their forecast describes. Why Can't the Agent Enforce Its Own Freeze? An instruction inside the agent's context isn't a control. The agent can't enforce a freeze on itself. Two public incidents from July 2025 show what happens when the agent's own judgment is the only control. In the first, SaaStr founder Jason Lemkin was running a public trial of Replit's agent during a code freeze. Fortune reported that the agent deleted a live database covering over 1,200 executives, ran unauthorized commands, and proceeded despite instructions to wait for human approval. It also told Lemkin that a rollback would not work, which Lemkin later showed was wrong. Replit CEO Amjad Masad called the deletion "Unacceptable and should never be possible." Lemkin later concluded that a code freeze cannot be enforced in these tools. In the second, a product manager reported that Google's Gemini CLI permanently deleted his files after misreading a failed directory creation. The AI Incident Database lists the report under Gemini CLI. The evidence is a user report, not a Google postmortem. In both incidents, the only thing between the agent and a destructive action was the agent's own judgment. OWASP makes the same point from the other side in LLM06:2025 Excessive Agency. It recommends human approval for high-impact actions and enforcing authorization in downstream systems, rather than trusting the model's judgment. The same principle, enforce outside the agent, is why Detect Unauthorized AI Agents Using Company API Keys puts detection at the key and the gateway rather than in the agent. The quarantine belongs in the file system and the supervisor, where the agent can see it but can't override, retry past, or delete it. What Happens to the Work When the Quarantine Lifts? The queue doesn't stop when the agent does. A quarantine doesn't pause the world. Scheduled and queued actions keep piling up while the agent is blocked, so when a human clears the sentinel, the backlog is waiting. If it drains all at once, the quarantine has manufactured the very burst it was meant to prevent. Two rules follow. The quarantine record should show the queue depth, so whoever clears it knows how big the release will be. And clearing should drain the queue at the normal rate, not all at once. The same rate-limit-on-release logic applies to spawned workers, which Limit Subagent Spawning: Recursion Depth Isn't a Budget covers: bound how much work can start at once, not just how deep it can nest. What Did the Lockout Check Actually Get Wrong? On 2026-07-21 the lockout check returned True, and the loop skipped one action and carried on. The check did its job. The mistake was treating the detector as the consequence. The consequence is the quarantine, and it has to live outside the process it stops.
Source: Dev Community
A light beam is focused through a lens into thin wires that pass through panels featuring increasingly complex patterns of blue dots, dashes, and other geometric shapes. The Google G is on the bottom left corner.
Source: The Keyword
Chief Operating Officers (COOs) use DMart insights, Quick Commerce, and Artificial Intelligence Tools to expand retail stores while protecting margins and boosting profits.
Source: Analytics And Insight
Few questions in fitness truly have a "right" or "wrong" answer.
Source: Lifehacker
AI-assisted development produces a lot of exhaust. A reviewer flags something out of scope. A workaround goes in "for now". The agent finds a bug in a file it wasn't asked to touch. I correct its approach for the third time. In most projects, that exhaust evaporates. In ParkEase, it had five places to land, and a hook that wouldn't let a task end until it had. Five harvest bins 1. learnings.md: fixes that cost time. Covered in post 6: 246 entries, read before every task. 2. suggestedtask.md: things found and deliberately not done. Every time the agent noticed something worth fixing that would bloat the current change, it added a row: where it was found, why it was deferred, and what "done" means for it. By the end, 162 findings had been logged this way. The rule behind it: Keeping a task the size it was scoped to be is right; dropping the finding on the floor is not. That one rule ended "I noticed X but didn't mention it", which is where AI-assisted projects quietly rot. Rows get closed later, in batches, by whichever task naturally touches that code. 3. ponytail: comments: shortcuts with a label. When the agent deliberately cut a corner, the code says so, names the ceiling and the upgrade path (post 4). Debt you can grep for isn't hidden debt. 4. Decision records: structural choices. 34 of them. When a tool or a skill gave advice that contradicted a decision, the decision won and the agent had to say so. When a decision changed, it was amended in writing, never silently. 5. Memory: corrections to how the AI works. Claude Code keeps a small per-project memory. Every time I corrected the agent's behaviour, not just its code, that correction became a memory with the reason attached. A few that stuck: Research first. After it guessed four wrong prop names on a map library one at a time, instead of reading the type definitions once. Run gates, don't narrate them. After it wrote "Security: raw-body HMAC, amount re-fetched" into a checklist (a description of the code, not a review) and called the review done. Token budget over ceremony. After about 40 reviewer agents on one task ate most of a week's usage limit. Twelve of the project's memories are behavioural corrections like these. Each one is a mistake that happened exactly once. The hook that enforces it Good intentions don't survive a long session, so harvesting is enforced by a hook: a script that runs whenever the agent tries to finish. If the task was complex (three or more file edits) and none of the learnings, test-case or decision files were touched, the agent isn't allowed to stop. It checks the files' modification times, so intent doesn't count; only a written entry does. The same hook also watches the agent's final message for the classic rationalizations, phrases like "this is a pre-existing issue", "skipping tests for now", or "tests are failing but I'll fix them later". Saying one doesn't end the task; it blocks it. What harvesting bought The second half of the project was noticeably smoother than the first, and it wasn't because the model changed. The repo had learned. Traps that cost an hour in week one cost a four-second read in week four. Review findings didn't vanish. And the agent's worst habits, guessing, narrating, over-dispatching, each got corrected once and stayed corrected. The model forgets everything every session. The project doesn't have to. Next (bonus posts): the series continues with stories I didn't plan to tell, starting with being offered 292 skills and installing 21. ParkEase is a peer-to-peer parking marketplace for India, built solo with Claude Code. Code: https://github.com/Deonkar/parkease
Source: Dev Community
The average lifespan of a technology product is three years. Not the company — the product. Most consumer applications, SaaS platforms, and enterprise software tools either undergo such significant changes within three years that they are effectively different products, or they are discontinued. Three years is not long enough to matter in agriculture. It is not long enough to matter in medicine. It is not long enough to matter in a business that has just rebuilt how it runs. The systems that govern these domains operate on decade-scale time horizons. A farmer building soil health over twenty years cannot afford to have the diagnostic tool they depend on become unsupported in year four. A distributor that has moved its entire order book, its credit terms, and its stock records onto one system cannot have that system change shape underneath it. This creates a specific obligation for anyone building technology in these domains. Not the obligation to be innovative — that is easy. The obligation to endure. What Endurance Requires A system that is genuinely designed to endure is different in its architecture, its business model, and its organisational philosophy from a system designed for growth and exit. Architecturally, enduring systems are built with a conservatism about dependencies that fast-moving product teams rarely exercise. Every external dependency — a third-party API, a cloud service, a database provider — is a potential point of failure in ten years. Systems designed for longevity either own their core infrastructure or are designed to be portable across infrastructure providers with minimal friction. They use open standards where open standards exist. They document their internal workings in ways that allow a different team to understand and maintain the system if the original team is no longer available. In their business model, enduring systems are designed so that the incentives of the builder align with the long-term interests of the user. A system whose revenue depends on users continuously upgrading to new versions has an incentive to make old versions less functional. A system whose revenue comes from genuinely useful ongoing service has an incentive to keep the system working well. The alignment between commercial incentive and user interest is a feature of the business model, not a moral position. Philosophically, organisations that build enduring systems have made a decision about what they optimise for. They optimise for reliability over novelty, for correctness over speed, for trust accumulated over years rather than attention accumulated over days. These are not natural alignments for a technology company in an industry that rewards growth metrics and exit valuations. They require deliberate choices about what kind of institution to be. The Failure Mode of Short-Cycle Technology The failure mode of systems that are not designed to endure is visible throughout the technology landscape in India's critical sectors. Digital agriculture platforms have launched, attracted farmers to their systems, accumulated data about farming practices and market conditions, and then shut down or pivoted when funding conditions changed. The farmers who had adopted these platforms were not just inconvenient — they were sometimes in a worse position than if they had never adopted the platform, because they had made decisions based on data and advice that was no longer available, and the institutional knowledge they had built around the tool was now useless. Electronic health record systems in district hospitals have been implemented, under-supported, and eventually abandoned — leaving clinicians with hybrid paper and digital workflows that are worse than either would be alone. Agricultural input platforms have changed their terms of service, their pricing structures, and their product offerings in ways that made business sense for the company but created operational disruption for the farmers who depended on them. These failures are not failures of bad actors. They are the predictable outcome of building technology for contexts that require decade-scale commitment with business models and organisational cultures optimised for eighteen-month cycles. The Truffaire Commitment When we deploy ARCORA with a farmer producer organisation, we are not deploying a product. We are establishing a relationship with a time horizon that extends well beyond the current growing season. The data that accumulates through ARCORA — the disease records, the treatment outcomes, the seasonal patterns, the crop-specific knowledge — is not data that we extract for our commercial benefit. It is institutional knowledge that belongs to the FPO and its member farmers. Our obligation is to make that knowledge more accessible and useful over time, not to own it. When we configure SPEXA for a business, we are building the record that business will reason from — its stock, its ledger, its customer history — for as long as it runs on the system. A figure entered in 2025 has to still mean the same thing when someone reads it back in 2030. That is not a feature request. It is the condition under which the record is worth keeping at all. This is not a constraint we resent. It is the core of what makes the work meaningful. Building systems that matter means building systems that last. And building systems that last means accepting the disciplines, constraints, and commitments that the technology industry has largely decided are incompatible with fast growth. We have decided they are not. The two things can coexist. The decision to prioritise permanence is not a decision to grow slowly — it is a decision to grow in a direction that produces something worth keeping. Originally published at truffaire.in.
Source: Dev Community
North Korea has dismissed US allegations that it was responsible for a $6 billion cryptocurrency theft from Binance, calling Washington’s claims an act of political provocation and accusing the United States of trying to damage its international reputation. Kim Yo Jong, the influential sister of North Korean leader Kim Jong Un, described the allegations as […] The post Is North Korea Using the Binance Heist Dispute to Signal US Hostility? appeared first on Modern Diplomacy.
Source: Modern Diplomacy
US President Donald Trump and congressional candidates made their final appeals to voters on Monday as tensions rose across several states ahead of midterm elections that could determine control of Congress and shape the final two years of Trump’s presidency. The final campaign day was marked by political disputes in Michigan and California, where Democratic […] The post Can Democrats Take Control of Congress as US Midterm Elections Begin? appeared first on Modern Diplomacy.
Source: Modern Diplomacy
More than 14 years after a 15-year-old Northern California girl vanished on her way to school, the man previously convicted of kidnapping and murdering her is back on trial after an appeals court overturned his conviction earlier this year. Antolin Garcia-Torres, 35, appeared in a Santa Clara County courtroom Monday for the start of his [...]
Source: Ktla5