181 Tech claim verifications avg. score 5.9/10 99 rated true or mostly true 63 rated false or mostly false
“A smartphone camera autofocus system needs to achieve focus in under 100 milliseconds.”
The evidence does not support a universal under-100 ms autofocus requirement for smartphone cameras. Reliable sources show a wide range of autofocus and capture times, with many working devices operating above 100 ms while remaining commercially normal and usable. Sub-100 ms is better described as a fast, high-end target under favorable conditions than as a system-level necessity.
“A typical smartphone is roughly 8 millimetres thick.”
The evidence supports this as a sound rule of thumb. Official specifications from major manufacturers place many mainstream smartphones around 7.6-8.3 mm thick, making “roughly 8 millimetres” a fair description of a typical handset. Thickness varies by segment, and quoted dimensions usually exclude camera bumps, but those details do not change the basic picture.
“A smartphone camera lens physically moves forward or backward to focus on objects at different distances.”
The core explanation is correct: most autofocus smartphone cameras focus by moving a lens element or lens group slightly forward or backward. The statement is too broad, though, because some phone cameras are fixed-focus and some newer designs can change focus without the same mechanical movement. The practical takeaway remains accurate for most modern autofocus phone cameras.
“A piezoelectric motor can mechanically hold its position when power is cut (off-power holding), whereas a voice coil motor (VCM) requires current to hold position.”
The claim captures the usual engineering distinction. Many piezo motors can hold position off power through frictional or self-locking mechanics, whereas standard voice-coil motors are back-drivable and typically need continuous current to hold force or maintain position under load. The caveat is that this is not universal: some piezo-based actuators are not mechanically self-locking, and specialized VCM systems can achieve zero holding current with added design features.
“In a voice coil motor (VCM), the magnetic field is provided by a permanent magnet, and the lens position is controlled by varying the current through a copper coil.”
The statement accurately describes the basic operating principle of a voice coil lens actuator. In standard VCM designs, a permanent magnet supplies the static magnetic field, and changing current in the coil changes the force that moves the lens. Some implementations add springs, biasing, or feedback, but those details do not negate the claim’s core mechanism.
“A voice coil motor (VCM) autofocus module typically uses a return spring so that when current is reduced or cut, the lens moves back toward a rest position.”
The claim matches how most mainstream VCM autofocus modules are designed. Technical sources describe the lens carrier as suspended by springs or flexures, with position set by the balance between magnetic drive force and restoring force. When current is reduced or removed, the lens typically returns toward a default rest or park position, though some less common VCM variants behave differently.
“Piezoelectric autofocus motors can hold their position without continuous electrical power, unlike voice coil motor (VCM) autofocus modules that require continuous current to hold a non-rest position.”
The central distinction is broadly correct: piezoelectric autofocus motors are commonly able to hold position with power removed, while conventional VCM autofocus modules usually need current to hold away from their rest position. The caveat is that this contrast is not universal, because specialized VCM autofocus designs have been engineered for zero holding current.
“Microsoft instructed approximately 100,000 of its engineers to stop using an AI coding tool by the end of June 2026.”
Microsoft did tell some engineers to stop using Claude Code by the end of June 2026, but the evidence does not support the claim that this applied to about 100,000 engineers. Reliable reports describe a division-level change affecting thousands, not a company-wide order across Microsoft’s engineering workforce.
“Microsoft instructed its engineers to stop using an AI coding tool because the tool's usage costs were higher than the cost of paying the engineers.”
Microsoft did reportedly curb internal use of Claude Code and move engineers to GitHub Copilot CLI because costs were rising. But the stronger claim that the tool’s usage cost more than paying the engineers is not established by the available evidence. That salary comparison appears to come from a narrower contextual remark, not a documented company-wide finding or the clearly stated basis for the policy.
“An exhaust camshaft sprocket synchronizes camshaft rotation so that engine valves open and close at the correct times to expel burned gases from the engine.”
The statement accurately describes the exhaust camshaft sprocket’s role in engine timing. It helps keep the camshaft synchronized with the crankshaft so exhaust valves operate at the right points in the cycle, allowing burned gases to be expelled. The main caveat is that exact valve events depend on the full timing system and cam geometry, not the sprocket alone.
“In an internal combustion engine, the timing chain synchronizes the crankshaft and camshaft(s) so that piston motion is coordinated with the opening and closing of the intake and exhaust valves.”
The claim correctly states the timing chain’s core job. In chain-driven internal combustion engines, it mechanically synchronizes crankshaft and camshaft rotation so intake and exhaust valves open and close in coordination with piston movement. Electronic controls and variable valve timing may fine-tune that relationship, but they do not negate the chain’s basic synchronizing role.
“In a typical hydraulic valve lifter, the plunger automatically adjusts to eliminate clearance in the valvetrain.”
The statement accurately describes the normal function of a hydraulic valve lifter. In typical designs, the internal plunger uses oil pressure and spring force to take up lash and maintain near-zero valvetrain clearance during operation. Manual preload is still required at setup, but that does not negate the lifter’s automatic self-adjusting action once correctly installed.
“In a typical hydraulic valve lifter, the body is the outer casing.”
The evidence supports the ordinary technical meaning of the term. In standard hydraulic valve lifter descriptions, the body is the outer cylindrical casing that houses the internal plunger, spring, and check-valve components. A niche alternate design uses different housing/body terminology, but that does not materially change what is typical.
“In a hydraulic valve lifter, a check valve allows engine oil to flow into the lifter's high-pressure chamber but prevents it from flowing back out when the camshaft lobe loads the lifter to open the engine valve.”
Technical sources consistently support the described mechanism. In a hydraulic valve lifter, the check valve admits engine oil into the pressure chamber and closes when the lifter is loaded, preventing reverse flow so the trapped oil can transmit motion to open the valve. Some designs also rely on oil-gallery port misalignment under load, but that is an added detail, not a contradiction.
“An oil reservoir system is used to maintain zero valve clearance in an internal combustion engine valve train.”
The evidence shows that hydraulic valve-train components use an internal oil reservoir or chamber to keep valve lash at zero or near zero during operation. Technical sources consistently describe this as the operating principle of hydraulic lash adjusters or hydraulic tappets. The phrasing is somewhat simplified, but the underlying mechanism is accurately stated.
“More than 30% of newly written source code in the United States is produced using AI coding tools.”
The evidence does not substantiate a nationwide figure above 30%. Broad, cross-organizational estimates cited in the record cluster just below that mark, while higher percentages mostly come from exceptional firms such as Google or from narrower measurements that do not represent all newly written U.S. code. The claim also mixes AI-assisted coding with code actually generated by AI, which can inflate the apparent share.
“Rocky Mountain Power redirected all electricity generation capacity it owns to provide backup power for a newly built AI data center in Utah.”
Available evidence does not support any diversion of Rocky Mountain Power's entire owned generation fleet to one Utah AI data center. The relevant utility filings and Utah regulatory materials describe a large-load service arrangement with customer cost protections, not exclusive backup service from all utility-owned generation. Reporting on Utah data centers instead indicates these projects often need new or self-supplied power because existing utility capacity cannot simply be reassigned wholesale.
“Substantive disagreements between AI models on fact-checking outcomes are common.”
Evidence from multiple studies shows that AI fact-checking models often reach materially different verdicts on the same claim, with reported substantive conflicts commonly in the roughly 15% to 30% range on challenging datasets. That is frequent enough to count as common in real-world use. Rates do vary by claim difficulty, ambiguity, prompting, and evidence quality.
“As of Q1 2026, frontier AI coding models exceed expert human performance on real-world software engineering tasks, as demonstrated by SWE-bench Verified and HumanEval+ results.”
Available evidence does not show that frontier AI coding models outperform expert humans on real-world software engineering as of Q1 2026. Very high scores on SWE-bench Verified and HumanEval+ are not direct expert-versus-model comparisons, and HumanEval+ is a weak proxy for real software engineering. Independent analyses also report contamination, benchmark artifacts, and many supposedly successful patches that human maintainers would reject.
“Claude AI cannot directly crack Bitcoin encryption or hack into a blockchain.”
Available evidence shows Claude cannot break Bitcoin’s secp256k1 cryptography or penetrate the Bitcoin blockchain by itself. Stories about AI “cracking” wallets refer to password recovery, file analysis, or other user-side help, not a break of Bitcoin’s protocol. The main caveat is that AI can still assist attacks on users, wallets, exchanges, or vulnerable blockchain software.