OpenAI is testing a new always-on assistant called “o”, and references to the unannounced feature briefly appeared online.

OpenAI hasn’t confirmed or denied that it’s working on a new always-on assistant, but as some users spotted on X, “o, your always-on assistant” was briefly listed as one of the benefits of the $100 ChatGPT Pro plan.

This feature was listed alongside more Work and Codex usage, maximum memory, 100GB of file storage, and early access to new features.

Moreover, there’s a dynamic configuration with a reference to display_name: "o" alongside an email_suffix: "-o", and this seems to suggest that the assistant could eventually have some form of email capability or identity.

That makes sense for an “always-on” assistant, which could continue helping with your tasks when you’re not actively using ChatGPT, but we don’t yet know exactly what OpenAI has planned.

OpenAI could reveal more at DevDay

OpenAI has already confirmed it’s hosting DevDay 2026 on September 29 in San Francisco, and it’s expected to announce a number of new features.

The event would give us a closer look at what its teams are building, along with technical sessions, demos, and new tools, so I wouldn’t be surprised if we hear more about “o” there.

There have also been references to an internal “Aeon” system around custom agents for ChatGPT workspaces, but “o” appears to be a separate consumer-facing experience.

Join Mikko Hyppönen and security leaders from the NFL, CHANEL, and Atlassian for a two-hour digital summit on what AI-speed attacks change, what defenders should stop doing, and how to validate, decide, fix, and re-validate at machine speed.

Save your seat

The staggering $900 discount delivers the lowest price on record for the M5 Max MacBook Pro 16-inch.

Amazon has issued a steep price cut on Apple’s high-end 16-inch MacBook Pro with an M5 Max chip. This laptop in Apple’s silver finish has an 18-core CPU and 32-core GPU, along with 36GB of unified memory and a spacious 2TB SSD.

Save $900 on 16″ M5 Max MacBook Pro

Retailing for $4,399, the $900 discount brings the price down to $3,499, the lowest on record. To put the value of the deal in perspective, Amazon is charging $300 for the exact same spec in Space Black.

Units are in stock and ready to ship at press time, with delivery as soon as tomorrow for Prime members depending on your delivery address.

You can find this deal and more in our 16-inch MacBook Pro Price Guide, with highlights across the range below.

16-inch MacBook Pro deals

• 16″ MacBook Pro M5 Pro (18C CPU, 20C GPU, 24GB, 1TB, Standard Display): $2,814 ($185 off)
• 16″ MacBook Pro M5 Pro (18C CPU, 20C GPU, 48GB, 1TB, Standard Display): $3,367 ($232 off)
• 16″ MacBook Pro M5 Max (18C CPU, 32C GPU, 36GB, 2TB, Standard Display): $3,499 ($900 off)
• 16″ MacBook Pro M5 Max (18C CPU, 40C GPU, 48GB, 2TB, Standard Display): $4,499 ($500 off)

14-inch MacBook Pro sale prices

• 14″ MacBook Pro M5 (10C CPU, 10C GPU, 16GB, 1TB, Standard Display): $1,839 ($160 off)
• 14″ MacBook Pro M5 Pro (15C CPU, 16C GPU, 24GB, 1TB, Standard Display): $2,351.99 ($148 off)
• 14″ MacBook Pro M5 Pro (18C CPU, 20C GPU, 24GB, 2TB, Standard Display): $2,989 ($210 off)
• 14″ MacBook Pro M5 Max (18C CPU, 32C GPU, 36GB, 2TB, Standard Display): $3,499 ($600 off)

The vinyl revival has created no shortage of affordable turntables, but listeners still need reasonably priced phono preamps from companies that actually know how to design them. Space is another consideration, especially as more people build desktop, bedroom, and smaller living-room systems where another full-width component is not particularly welcome.

Clearaudio is addressing both issues with the new Compact Phono, an external phono stage measuring just 80 x 115 x 26 mm (3.15 x 4.53 x 1.02 inches). Despite its pocket-sized dimensions, this is not a stripped-down accessory designed merely to get a turntable working. The Compact Phono supports both Moving Magnet (MM) and Moving Coil (MC) cartridges and is designed to deliver the performance expected from a company with decades of experience building turntables, cartridges, and phono stages.

For vinyl listeners looking to improve upon the phono stage built into an amplifier or turntable without adding another large box to the system, that combination of flexibility, pedigree, and space-saving design makes the Compact Phono considerably more interesting.

Active Feedback

At the heart of the Compact Phono is what Clearaudio calls an Active Feedback Topology, built around extremely low-noise, high-precision operational amplifiers. Rather than asking a single gain stage to do everything, amplification is divided across two stages. The first provides most of the gain, which helps preserve the very small signal coming from the cartridge while maximizing the signal-to-noise ratio.

The second stage handles additional filtering at both ends of the frequency spectrum. A 16Hz subsonic filter reduces very low-frequency energy caused by record warps and turntable rumble, while a 70kHz ultrasonic filter is designed to suppress high-frequency cartridge resonances that can compromise stability even though they sit well above the audible range.

The practical benefit is lower noise, better control of unwanted frequencies, and a more stable signal path while maintaining the required RIAA equalization. That is particularly important with low-output moving-coil cartridges, where the phono stage has to provide substantially more gain without turning background noise into part of the performance.

Maximum Flexibility for MM and MC

The Compact Phono is designed to work with everything from conventional Moving Magnet cartridges to low-output Moving Coil designs. In MM mode, input loading is fixed at 47 kΩ / 100 pF, while MC mode uses 100 Ω / 100 pF. Gain is set at 40 dB for MM and 60 dB for MC, providing the additional amplification required by the much lower output voltage of most Moving Coil cartridges.

Those gain figures are higher than Clearaudio offered with some of its previous compact phono stages, but the more important specification may be the 29 dB of headroom in MC mode. Headroom determines how much signal the phono stage can accommodate above its normal operating level before overload becomes an issue. With highly dynamic recordings and cartridges capable of producing larger transient peaks, that extra margin reduces the likelihood of the input stage being pushed into clipping or distortion.

The practical advantage is straightforward: the Compact Phono provides sensible loading and gain for both major cartridge types without requiring internal adjustments, while offering considerably more operating margin than its tiny enclosure might suggest.

Construction and Power

Clearaudio designed a dedicated power supply for the Compact Phono that delivers ±18 V to the phono stage, with two linear voltage regulators used to provide a stable, low-noise supply to the audio circuitry. Inside, the electronics are built around a four-layer PCB with separate ground and power-supply planes, an approach intended to keep signal paths short, reduce impedance, and limit unwanted electrical interference.

Selected thin-film resistors and high-quality capacitors with tight tolerances are used throughout the circuit. In a phono stage, component consistency is particularly important because small variations can affect RIAA equalization accuracy and channel matching. The electronics are housed in a low-resonance metal enclosure with isolating feet that provide additional mechanical protection.

Power efficiency has also improved significantly. The Compact Phono consumes just 0.7 watts during operation, compared with 2.3 to 2.7 watts for Clearaudio’s predecessor models. With consumption already this low, there is no separate standby mode.

The result is a compact phono stage that generates less heat and consumes considerably less power while still using the regulated power supply, carefully laid-out circuitry, and tightly specified components expected from a serious MM/MC design.

Clearaudio Compact Phono Preamp Specifications

The Bottom Line 

At $639, the Clearaudio Compact Phono is neither the least expensive MM/MC phono preamp nor the most adjustable. The iFi ZEN Phono 3 costs $249 and offers four gain settings from 36 to 72 dB, multiple loading options, balanced output, and an intelligent subsonic filter. The $249 Andover Audio SpinStage provides extensive MM loading adjustments and a current-mode MC circuit, while Cambridge Audio’s $399 Alva Duo supports MM and MC cartridges and adds a built-in headphone amplifier. Pro-Ject also offers several MM/MC alternatives with considerably more cartridge-loading flexibility, including its balanced Phono Box S3 B and tube-based Tube Box S3 B.

So what makes the Compact Phono interesting? It packs serious MM/MC capability into an exceptionally small 80 x 115 x 26 mm enclosure without making everyday operation unnecessarily complicated. Switching between MM and MC is handled externally rather than through internal jumpers, while 40 dB and 60 dB gain settings, 29 dB of MC headroom, 16Hz subsonic and 70kHz ultrasonic filtering, a regulated ±18V power supply, and just 0.7 watts of power consumption suggest that Clearaudio concentrated its engineering effort on noise, headroom, stability, and efficiency rather than loading menus and rows of switches.

That makes the Compact Phono less about winning a specification war and more about delivering Clearaudio’s analog engineering in a space-saving design that can sit beside almost any turntable without taking over the shelf. The iFi and Andover models remain formidable values, while Pro-Ject offers greater adjustability for listeners who regularly experiment with cartridges. The Clearaudio makes the stronger case for someone who wants a compact, straightforward MM/MC phono stage from a company that has been designing serious analog playback equipment for decades.

Price & Availability

The Clearaudio Compact Phono is available in black and silver from authorized dealers at a suggested retail price of $639 (US) / £490 / €490. 

Related Reading:

Strands is the NYT’s latest word game after the likes of Wordle, Spelling Bee and Connections – and it’s great fun. It can be difficult, though, so read on for my Strands hints.

Want more word-based fun? Then check out my NYT Connections today and Quordle today pages for hints and answers for those games, and Marc’s Wordle today page for the original viral word game.

AI job matching turns information about a candidate and a job into signals, narrows the available pool, scores plausible matches, and ranks the results. More advanced systems can also use semantic similarity, behavior, and feedback, but a high-ranked match is still an estimate of relevance rather than proof that a person and job are objectively compatible.

There is no single algorithm that every job platform uses. Different systems can use different data, models, filters, ranking objectives, and feedback signals. The useful way to understand AI job matching is therefore as a common pipeline rather than as one universal formula.

AI Job Matching Is a Ranking Problem, Not a Mind Reader

AI job matching does not understand a person’s career in the same way a human recruiter or career adviser might. Instead, software converts available information about people and vacancies into data it can compare.

A signal is a piece of information a system can use when estimating how relevant a candidate and job appear to each other. Signals can come directly from profile fields, job postings, resumes, search preferences, or interactions with the platform.

Consider a nurse searching for a remote role. The candidate side might contain a nursing qualification, years of experience, location, preferred work arrangement, and listed skills. A vacancy might contain a title, required license, location rules, schedule, experience requirements, and employment type. A matching system can compare some of those details and use the result to narrow or order vacancies.

The same general idea works in the opposite direction when recruiters search for candidates. LinkedIn’s current Recruiter documentation describes a process in which candidates matching requested keywords and facets are selected and then ranked using additional signals such as work-experience or skill similarity and likelihood of response.

One important limitation appears before any sophisticated ranking occurs: both sides can be incomplete. A candidate may omit a relevant skill or certification, while a job description may leave an important requirement unstated. Missing information therefore does not necessarily mean the candidate lacks a qualification or that the job lacks a characteristic.

Step 1: Turn Profiles and Job Posts Into Matching Signals

The first technical challenge is converting messy information into signals the system can actually compare. These signals fall into several broad categories, although individual platforms can define and combine them differently.

Explicit signals

Explicit signals are information that a user, recruiter, employer, or platform records directly. Examples include job title, location, employment type, degree, certification, salary preference, years of experience, and whether a person wants remote, hybrid, or on-site work.

LinkedIn’s current job-recommendation documentation says recommendations can use preferences including job titles, locations, location types, and employment types. It also says profile-based recommendations can consider information from a member’s headline, About section, experience, education, and location preferences.

Explicit information is useful because its meaning can be relatively clear. If a person selects remote work, the platform does not need to infer that preference from unrelated behavior. The limitation is that explicitly entered information can be incomplete, outdated, inconsistent, or too broad.

Inferred and semantic signals

Platforms can also derive signals from unstructured text. A skill, occupation, or concept may appear in a resume, profile summary, work history, or job description without being stored as a neatly selected field.

Semantic matching means comparing meaning or related concepts rather than depending only on identical text. For example, “SaaS” and “Software as a service” can describe the same concept even though the wording differs.

LinkedIn’s current Skills filter distinguishes explicit skills from implicit skills extracted or inferred from profile content. It also gives a concrete example in which a search for “SaaS” can return members whose profiles use “Software as a service” instead.

Semantic inference can expand useful matches, but it creates another failure mode: an inferred skill or concept can be wrong. Mentioning a technology in a project description does not always mean the person is proficient in it, and similar terminology can have different meanings in different industries.

Behavioral signals

Interactions can become signals too. Saving a vacancy or candidate, hiding a recommendation, messaging someone, applying for a job, or repeatedly searching for a particular role can provide evidence about likely interest or responsiveness.

LinkedIn’s Recommended Matches says recruiter actions such as saving, hiding, and messaging candidates are used as real-time hiring signals. It also describes job-posting signals such as titles, locations, and skills and member-side signals such as being open to work.

Behavior is useful but ambiguous. A user may ignore a job because it is irrelevant, because important information is missing, because they have already seen it, or simply because they never noticed it. Treating every interaction as a perfect description of preference can therefore teach a system the wrong lesson.

The table below separates the main signal categories from what they can reasonably tell a matching system. Every signal is a proxy for something the platform wants to estimate rather than a complete description of the person or job.

Step 2: Apply Constraints and Retrieve Plausible Matches

After signals exist, a platform does not necessarily run its most detailed scoring process against every possible person-job pair. Large marketplaces can contain enormous numbers of profiles and vacancies, so matching can begin by reducing the search space.

A hard constraint is a condition treated as required for a particular search or matching operation. Depending on the system and user-selected filters, examples can include a location requirement, employment type, occupational credential, or another must-have condition.

Retrieval is the stage that finds a manageable set of plausible candidates or jobs before more detailed ranking. It answers a broad question such as “Which possibilities are worth evaluating further?” rather than “Which one should appear first?”

LinkedIn’s current recruiter documentation describes this separation directly: candidates matching requested keywords and facets are selected and then ranked using other factors. Its current AI Search documentation also says natural-language requests can be translated into structured filters such as location, skills, and job titles.

This distinction creates an important failure point. If a strict filter removes someone during retrieval, the later ranking system may never evaluate that person. A qualified candidate can therefore disappear because a requirement was entered too narrowly, a profile field is missing, a job title is unusual, or an equivalent qualification is represented differently.

Retrieval quality matters as much as ranking quality. A ranking model cannot promote a relevant result that was never retrieved.

Step 3: Score and Rank the Remaining Matches

Once the system has a smaller set of plausible matches, it must decide which results deserve the most prominent positions.

A relevance score is a model-produced value used to compare matches under the system’s chosen objective. Its exact meaning depends on the product. A score might reflect similarity, predicted responsiveness, expected usefulness, or a combination of signals.

It should not automatically be interpreted as a probability of being hired.

Current LinkedIn Recruiter documentation provides a concrete example of multi-signal ranking. It says candidates can be ranked using factors including similarity between their work experience or skills and the search criteria, together with the likelihood that an interested candidate will respond. LinkedIn says those factors are weighted using machine-learning models.

A simplified matching system might face a trade-off like this:

• Candidate A has very strong skills similarity but does not satisfy the preferred location.
• Candidate B has slightly weaker skills similarity but matches the location and employment preferences closely.
• Candidate C satisfies the stated search criteria but appears less likely to respond.

These are illustrative examples, not LinkedIn’s published scoring formula. The final order depends on the objectives and weights used by a particular system. Commercial platforms usually do not disclose a fixed percentage for every signal, so it would be misleading to assign arbitrary weights to skills, location, experience, or activity.

Some recommendation architectures use more than one ranking pass. Reranking means taking an already retrieved or initially ordered set and applying another model or scoring stage to refine the order.

A 2026 job-recommendation study, for example, combined metadata filtering, TF-IDF lexical retrieval, Sentence-BERT semantic retrieval, and optional cross-encoder reranking. The research demonstrates one possible hybrid architecture; it does not establish that LinkedIn, Indeed, Lensa, or every commercial job platform uses Sentence-BERT, cross-encoders, or the same model sequence.

Why Keyword Matching and Semantic Matching Produce Different Results

Keyword matching and semantic matching solve related but different problems.

Lexical or keyword matching looks for words, phrases, or standardized terms. It works well when a candidate and job use the same vocabulary. If both contain “project manager,” the connection is straightforward.

The weakness appears when equivalent ideas use different language. A job might request “Software as a service” experience while a candidate writes “SaaS.” A narrow exact-text approach could miss that relationship.

Semantic matching attempts to capture related meaning so concepts can still appear similar without identical wording. This can help retrieve profiles or vacancies that strict text matching might miss.

The two approaches do not have to compete. The 2026 research system cited above combined lexical and semantic retrieval rather than replacing one with the other. Structured filters can preserve precise constraints while semantic retrieval can broaden matching across terminology differences.

Semantic matching also has limits. Two phrases can appear related to a model while carrying different practical meanings. “Machine learning research” and “machine learning operations,” for example, overlap in vocabulary and domain but can require substantially different experience. Better semantic recall therefore does not automatically mean better job fit.

Job Matching Can Be a Two-Sided Recommendation Problem

Job recommendation differs from many ordinary recommendation systems because a useful match may depend on both sides of the marketplace. It is not enough for a job seeker to like a vacancy if the role has requirements the person does not meet, and an employer’s preferred candidate may have no interest in the location, schedule, salary, or role.

Researchers often describe this as a reciprocal recommendation problem because relevance can operate in both directions. A review of job recommender-system research identifies reciprocity as an important feature of the domain and also highlights data availability, temporal effects, fairness, and generalizability as evaluation challenges.

Consider two candidates for the same vacancy. Candidate A strongly prefers the job but lacks a mandatory professional license. Candidate B has the license and relevant experience but wants a fully remote role while the vacancy requires daily on-site work. A single similarity measure does not capture both sides of those constraints and preferences.

Indeed’s public explanation of its own matching system similarly describes a two-sided marketplace and a process of understanding people and jobs, narrowing and ranking possible matches, and learning from subsequent outcomes.

This does not mean every platform optimizes the same objective. A system might optimize for a useful recommendation, an application, a response, an interview, a hire, or another measurable outcome. Unless a platform documents its objective, the safest interpretation of a ranking is that the system estimates relevance or expected usefulness according to its own design.

Feedback Can Improve Rankings, but It Can Also Reinforce Mistakes

Matching systems can use what happens after recommendations are shown as additional information. A feedback loop occurs when earlier recommendations generate behavior that later influences future recommendations.

Possible feedback signals include clicking or saving a vacancy, hiding a recommendation, applying, replying to a recruiter, progressing to an interview, or being hired when the platform can observe that outcome.

Indeed says its system uses outcomes including applications, employer interview progression, ignored jobs, and successful hires as feedback for later matching. LinkedIn separately documents recruiter actions such as saving, hiding, and messaging candidates as signals in Recommended Matches.

This can make a system more responsive, but feedback is not neutral ground truth. Suppose a platform initially shows a software engineer mostly fintech jobs. If the person can interact only with the jobs the system exposes, later behavior may appear to confirm a strong fintech preference even if healthcare or education-technology roles were rarely presented.

This illustrates a filter-bubble risk. A 2025 LinkedIn research paper on large-scale job matching explicitly identifies cold start, filter bubbles, and bias as practical challenges in its research context. The paper describes a particular signal-integration system rather than establishing that every production job recommender experiences these problems to the same degree.

Cold start is the related problem that appears when the system has little historical information about a new user, new vacancy, or new type of role. In that situation, profile content, job metadata, skills, titles, or other non-behavioral signals can become especially important.

The Main Limitations of AI Job Matching

AI can make large job markets easier to search, but every stage of the pipeline introduces assumptions and possible errors. These are parallel failure modes, not a ranking of which problem is worst.

• Incomplete candidate profiles: a system cannot reliably use qualifications that are missing, outdated, or represented ambiguously.
• Weak job descriptions: vague titles, inflated requirements, missing conditions, or unclear working arrangements reduce the quality of the information being matched.
• Cold start: a new candidate, employer, vacancy, or occupation may have little interaction history from which to learn.
• Incorrect inference: semantic models can infer a skill, occupation, seniority level, or relationship that does not accurately describe the candidate or job.
• Feedback loops: future recommendations can become too dependent on what the system previously chose to expose.
• Optimizing the wrong outcome: more clicks or applications do not necessarily mean better jobs, stronger fit, better conditions, or successful hires.
• Ranking and exposure effects: higher positions receive more opportunity for attention, so ranking affects what users can see and act on.
• Bias and proxy variables: harmful patterns can enter through data, labels, system design, human processes, or variables correlated with sensitive characteristics.
• Changing labor-market patterns: occupations, terminology, skills, qualifications, and hiring behavior evolve, so relationships learned from older data may become less useful.
• Opaque scoring: users may see an ordered list or match label without knowing which signals dominated the result.

Bias deserves especially careful treatment. NIST’s current harmful-bias research page explains that bias can arise from more than training data and that AI systems can perpetuate or amplify harmful patterns. Removing one obvious sensitive field is therefore not enough to establish that a system is fair.

Ranking creates another issue: exposure. A result shown near the top has more opportunity to receive attention than one placed much lower. NIST’s TREC Fair Ranking work explicitly studies systems that balance result quality with fair exposure. That benchmark is not specific to employment, so it is used here only to illustrate the broader ranking principle that ordering affects who or what receives attention.

These concerns become more consequential when automation moves from recommending opportunities toward evaluating applicants. Job recommendation should therefore be separated from AI resume screening, where employer-side systems may evaluate or narrow applicant pools rather than simply recommend vacancies.

What a Match Score Does and Does Not Tell You

A high-ranked job or candidate generally means the system estimates that result to be more relevant under its available signals, filters, data, and optimization objective than at least some of the alternatives it considered.

That is useful, but it is narrower than many people assume. A high match score does not automatically prove that:

• the applicant satisfies every meaningful qualification;
• the employer will respond or offer an interview;
• the person will enjoy the role or workplace;
• the employer offers good conditions;
• the recommendation is free from bias;
• the score represents a probability of being hired; or
• the top-ranked vacancy is objectively the best opportunity available.

Real platforms show how these general ideas become product features. Lensa’s AI-assisted job matching, for example, provides a platform-specific case of AI-assisted job discovery and matching, while LinkedIn and Indeed document different combinations of preferences, profile information, search filters, behavioral signals, and ranked recommendations.

The safest way to use a match is as a search aid rather than a verdict. A ranking can reduce the number of possibilities you need to inspect, but broader job search strategies such as checking the employer, tailoring an application, reviewing requirements, and preparing for interviews still happen outside the matching score.

AI job matching is therefore most useful when its output is treated as an informed shortlist. The system can estimate relevance from the information it has, but the candidate and employer still provide context, verification, judgment, and decisions that a ranking cannot fully represent.

Cloudflare has fixed a vulnerability in Containers and Sandboxes that allowed customers with a Workers Paid account to recover residual data from other customers’ containers on the same physical host.

Cloudflare Containers is a service available on the Workers Paid plan that lets developers run containerized applications on Cloudflare’s infrastructure, alongside Cloudflare Workers.

Developers and companies building applications on Cloudflare typically use it, including those running backend services, processing jobs, and code execution environments.

The flaw was reported through HackerOne on September 4 by Oren Yomtov, a security researcher at technology company Accomplish.

Exploiting it would let an attacker read other customers’ files, including directory listings, SQLite databases, Chromium profiles, .env files, and credential files.

According to Cloudflare’s disclosure, the issue was in a shared storage pool configured to skip zeroing reused 64 KiB blocks.

“When the thin volume backing a container’s root disk was deleted, its physical blocks were returned to a pool that served workloads belonging to multiple customer accounts,” Cloudflare explains.

By writing only 4 KiB to an unused region of a new container’s disk, the researchers could cause a reused 64 KiB physical block to be allocated. Without the zeroing operation, only the 4 KiB write would overwrite the block, leaving in a readable state the remaining 60 KiB that may contain data from a previous customer.

They found residual material on 18 of 24 container placements and across 20 of 22 underlying nodes tested, including directory structures, database pages, and structurally complete SQLite databases.

“The vulnerability would potentially have allowed for a customer with a Workers Paid account to recover residual data from storage blocks previously used by other customers’ Containers on the same underlying host,” Cloudflare says.

“A successful exploitation would have crossed the tenant-isolation boundary and could disclose filesystem metadata, directory structures, database pages, and application data.”

An attacker would not have control over the victim or host, nor would they be able to read an actively attached disk.

Risk evaluation and real exposure

Cloudflare says the researchers only used scripts that performed checks and returned aggregate counts, not actual disk contents, so no real customer data was exposed in this evaluation.

The researchers also did not demonstrate any way to change another customer’s data or disrupt their workloads on Cloudflare’s service.

Cloudflare removed the setting that caused the skipped block zeroing, retired existing container disks, and cleared cached snapshots that may contain old mappings, finishing all mitigation actions by September 19, 2026.

After examining logs, telemetry, and historical data, the company found no evidence that customer data was exposed via the method described by Accomplish.

Cloudflare applied the fixes to its infrastructure automatically, and customers need to take no action to address the risk.

Join Mikko Hyppönen and security leaders from the NFL, CHANEL, and Atlassian for a two-hour digital summit on what AI-speed attacks change, what defenders should stop doing, and how to validate, decide, fix, and re-validate at machine speed.

Save your seat

The new form factor offers convenience at a premium.

Tablets still win for long sessions and serious work

What are the disadvantages of a foldable phone?

agustin.photo/Shutterstock

Nintendo shipped the Switch Pro, priced at $55.97 (was $80), a while back, the same day the original Switch arrived, and the shape has barely needed defending since. You get a one-piece gamepad measuring 106 millimeters tall, 152 millimeters wide, and 60 millimeters thick, weighing about 246 grams. Offset analog sticks sit in the familiar Xbox arrangement, with a plus-shaped direction pad under the left stick and the four face buttons above the right. Both sticks click. Capture, Home, Plus, and Minus land where muscle memory already lives. Plastic feels dense without turning heavy, and the grips stay comfortable through long docked sessions that split Joy-Cons never quite manage.

Nintendo estimates forty hours from its 1300mAh cell, and most people seem to take it at face value. It takes roughly 6 hours to charge it using a USB-C port and the accompanying USB-C cable, which also works with a dock if you prefer to play wired. With the accompanying USB-A adaptor, you have a great deal of freedom. Pairing continues to use the little Sync button on the top edge, as it has for quite some time. Meanwhile, the player lights have been located on the bottom edge. Several of these controllers can be connected to a single console at once.

Sale

Nintendo Switch Pro Controller

• Take your game sessions up a notch with the Nintendo Switch Pro Controller
• Handheld Nintendo Switch gaming at a great price
• Comes with charging cable (USB C to USB A)

The HD rumble in these handles continues to impress in the games that actually use it, as game designers appear to prefer showing off rains, footsteps, as well as engine noise as textures rather than a generic rumble. Motion sensing is important in games like Splatoon; it is also used in Zelda for bow aiming, and there are numerous other games that make use of it. Simply tap an amiibo near the Nintendo logo, and the NFC reader will take care of the rest. The fact that so many third-party controllers still fall short of this experience, even if their sticks appear to be fantastic on paper, speaks volumes about Nintendo’s design.

Of course, compatibility is the key to this controller’s lasting appeal. It works with the original Switch, the OLED, the Lite, and even the upcoming Switch 2, both wirelessly and via dock cable. The entire range of first-party Nintendo features are then carried over, including amiibo reading, gyro aiming, and the gorgeous HD Rumble. One minor exception remains: on a sleeping Switch 2, you cannot wake it up using the home button on this controller; instead, hit the main console button to get it starting. Steam Input on PC and official iOS 16 and later support from Nintendo itself will push the potential of this technology much further.

The analog sticks employ Alps potentiometers, which are similar to those seen in older controllers, so if you use this device regularly, you may notice some drift after a few years. The triggers are digital instead than analog. This one lacks a headphone jack, back paddles, and secret extra buttons; if those are important to you, you should look elsewhere. For the rest of us, the layout, battery life, and first-party features continue to make this an excellent pick for docked gaming with any switch model.

Paul Lagier builds spare, printable objects on YouTube, and Shiptracker began as a present. His girlfriend already followed cargo ships, tankers, and ferries on websites, reading destinations, speeds, and origins the way other people refresh a weather app. He wanted that ritual to live on a desk without a phone in hand, so he boxed a Raspberry Pi Zero W, a 7.5-inch Waveshare e-paper panel, and two rotary encoders into a pale 3D-printed shell that draws almost no power between screen updates.

Commercial trackers practically map almost every ship on the planet. You’d think MarineTraffic and other similar websites would provide an API you could use for your own small project, but they don’t, and scraping their pages is not an option. Finland managed to tackle their data challenge in a clever way. Fintraffic’s Digitraffic service simply broadcasts AIS locations and a plethora of other vessel metadata for free, at least for the Gulf of Finland, which extends between Finland, Estonia, and Russia. Norway and Singapore had similar feeds, but Finland’s stream was enough to generate a map with real ships on it.

Sale

Bambu Lab A1 mini 3D Printer + LED Lamp Kit, Ready-to-Use for Beginners

• A1 mini + LED Lamp Kit for Creative Light Projects: Bring your ideas to life with the included LED Lamp Kit. Simply print compatible lamp models and…
• The Perfect 3D Printer for Beginners: A1 mini 3D Printer is designed to make 3D printing easy from day one with automatic calibration, simple setup…
• Experience the Bambu Lab Ecosystem: Access MakerWorld’s huge library of ready-to-print models, manage prints through the Bambu Handy app, and enjoy…

Now, the map is being drawn using a Waveshare 7.5-inch E-Paper HAT V2. Having multiple shades of gray makes the coastline and ship markers readable, and the greatest thing is that the screen only needs energy when the image changes, making it very power efficient. Uploading partial black-and-white updates isn’t too difficult, but partial grayscale updates take longer. That provided Paul a reason to reconsider his design later. Paul had planned to use a Pi Zero 2 W, but it proved to be too expensive, so he purchased a used Zero W with the header pins already soldered on because it was a much cheaper alternative. That older board can still connect via Wi-Fi and run current software until 2026. However, it still utilizes the largely obsolete micro-USB power port, so he had to wire a USB-C breakout directly to the power pins and order a 5-volt 3-amp supply just to be safe.

Instead of a touchscreen, two buttons do the trick, each having an encoder that spins both directions and has a pleasing click. One knob scrolls among lists and views, while the other allows you to read details about a specific ship, such as its destination, projected arrival time, speed, length, and beam. There are two options: recommended mode displays the larger or more interesting vessels, and full list allows you to scroll through every ship that is currently reporting in. You can even mark one as a favorite with a single button press. After sitting inactive for a while, the interface will switch to watch mode, a calmer layout that only shows one ship, allowing you to have it on in the background without requiring continual attention.

Having images of each ship seemed like a fantastic idea at first, but it turned out that it wasn’t an option in the end. These full-screen grayscale refreshes simply took too long. Instead, Paul simply removed the photos and added a QR code to the detail view instead. If you scan the QR code, you can easily see a snapshot as well as all of the extra records on your phone. If you prefer a written summary of the ship, an AI-generated synopsis is included in the same package. Menus are accessible in both English and German.

He worked on the enclosure using Shapr3D. The front is recessed to fit the display, but a separate back plate just holds the Pi on four screws, preventing any pressure from being applied to the glass. He also used foam tape to keep the display from sliding about. He made a several prototypes in Panchroma Muted White PLA until the holes lined up correctly, then added a bottom cover and matching knobs. You may obtain the 3mf files for the housing, cover, mounting plate, and knobs, as well as an illustrated wiring guide, directly from the shop.
[Source]