#data integrity and validation testing

[onixcloud]

Cisco Enhances Security and Efficiency with Automated Data Validation Tools

Cisco successfully leveraged Pelican, a cutting-edge data validation tool, to enhance security and efficiency in its internal migration initiative. By implementing an automated data validation tool, Cisco eliminated manual validation, ensuring 100% secure data validation without data movement. This transformation resulted in zero test failure rate, validation of over 2 trillion rows, and organization-wide adoption, empowering 150+ data engineers. The collaboration between Cisco and Onix showcased the power of scalable data validation solutions, setting a new benchmark for data integrity and validation testing in large enterprises.

Click: https://www.onixnet.com/case-study/cisco-leverages-pelican-for-100-secure-data-validation/

[papercranesong]

Transparency in AI-use within Fandom Culture (or: how to be upfront when you risk getting shot down)

Since writing my original post, it’s been really cool hearing from fellow writers who use AI as a support tool to help them keep writing despite their mental health struggles, dyslexia, or in my case, depression.

There are valid and justified ethical concerns to do with the use of AI itself, such as the issue of consent, which I’ve tried to discuss elsewhere. But I wanted to write this post with fanfic writers and fandom in mind. There are some people like me who are already using it as a tool for writing, and so I wanted to look at how this can be done transparently and respectfully, and so that readers know and trust what they are reading.

Context

I’ve been using generative AI as a tool for over 18 months - initially as part of my work in the charity sector, and then later in writing fanfic. When my little one is older, I hope to go back into the field of Public Health, where I'll be using it as a tool to help analyse and synthesise qualitative and quantitative data (among a ton of other things), in order to help address health inequalities in the UK.

Perhaps naively, I didn’t fully understand the ethical concerns to begin with, particularly with regards to fanfic, and by the time I started realising there were issues it felt like there were no safe spaces in which to ask people about it.

Fear and loathing in Fandom Spaces

It seems like there’s this environment of fear and shame at the moment (the posts and reblogs that I see on my dash come across as absolutist, derogatory and even abusive towards anyone using AI for any reason), and I think this is why a lot of writers don’t want to be open about their use of AI, especially if they are in a small fandom and are worried what their mutuals or fellow writers and readers might think of them, or how they might get excluded from certain fandom spaces.

I’ve already seen some writing events that have a strict ‘no AI’ policy and whose language reflects the anti-AI sentiments above, so I can see why some people might join these events but not want to disclose their use of AI.  (Having said that, it’s not okay for people to enter an exchange undercover that has clear rules against AI, and to just stay silent and use it anyway. If an event or community has set boundaries, those need to be honoured and respected. We need to have integrity as AI users, and as a friend pointed out, respect has to go both ways).

Given that writers use generative AI for different reasons and in different ways, I think there needs to be a willingness to have an open and thoughtful conversation to reflect this spectrum of use. I’m just thinking off the top of my head – maybe a writing event could have these types of guidelines:

Whump-Mania Writing Event: AI Use Guidelines* 1. Be transparent. If you used AI (for ideas, research, grammar, etc.), mention it in your author notes. 2. Your words come first. AI can help but the story should be yours. No fully AI-written fic, please. 3. No purity tests. This is about honesty, not exclusion. Let’s keep the space kind and open.

(*For transparency: I asked chatgpt to come up with those guidelines, then I edited them. Also I made up the phrase Whump-Mania. As far as I know, there is no writing event called that, but it would be awesome if there was).

This is just a starter for ten, and would obviously need to be a lot more nuanced and thoughtful, especially in the context of gift exchanges, as people have varying degrees of comfort when it comes to accepting a gift where AI has been used in any aspect of writing it. (Personally, I’ve taken part in Secret Santa fic exchanges, and whilst I’d be fine with someone gifting me a work where they used AI to proof-read it, I would probably be a bit peeved if I found out they’d just taken my prompt, fed it into chatGPT and then gifted me that work).

So maybe some kind of tick box – “this is the level of AI-use I’m comfortable receiving” – ranging from ‘none’ to ‘fully-generated by AI’, with options in between. There would need a proper discussion, but I think it would be a worthwhile one so that these types of exchanges could remain inclusive.

(Just to point out again though, it’s up to the organiser at the end of the day - it’s their event and their hard work and time running it. If you’re unsure about their AI stance, it might be worth politely contacting them just to see what level of AI-use they might consider accepting, and sharing how you would use it - for example for spellchecking or research - and then politely accepting their decision without arguing or vagueposting about it, because they’re people too and it’s about remaining kind and respectful in this whole wider discussion, even if you feel hurt or misunderstood).

Tagging (or: my tag is not your tag)

So with regards to tagging – at the moment, I feel like tagging AI on AO3 isn’t a good option because there’s only one tag, “Created using generative AI”, which doesn’t distinguish between fully-AI generated works and one of my fics where I write every word and then use AI afterwards as a final spell-check before posting.

Also there’s a post going around on Tumblr at the moment that’s a screencap of the AO3 tag and listed works, and shaming people who have used the tag (although no individuals have been named). It’s got over 70,000 notes and it honestly feels a little scary.

Transparency can only work in an environment where people feel safe to speak (and tag), knowing they’re not going to get subjected to shame, hate and abuse. (Sorry for the jumpscare bold type. Just think that this is important to highlight).

Personal AI Disclaimer Use: (or, Me, Myself and AI)

So what I’m choosing to do is put an AI disclaimer use on my AO3 profile which gives me a voice to describe my own use of AI as well as advocating for more ethical AI. Then I’m putting a note in the author’s note of my fic saying “this fic was created in accordance with my personal AI disclaimer use, specifically - ” and then sharing how, e.g for research into mining duridium, a fictional ore in Star Trek.

This is the best I can come up with at the moment but I’d genuinely like to hear what other writers and readers think about it and if you have any suggestions – feel free to use the ask box (the anon function is on) or DM me. This is also why I’ve tagged this post with the fandom I’m currently writing in, for transparency and to get feedback.

It might be that because I use generative AI full stop, in any capacity, this means you’re not able to engage with my writing any more. I’m sorry for this but I do understand why you might feel like that. I appreciate your candour and wish you the Vulcan blessing of peace and long life and prospering in all you do.

Other people are understandably cautious about reading my fics going forward, and so that’s why I want to be transparent about the way I use AI, so that people can trust what they’re reading, and to make an informed decision about whether or not to engage with the story.

In conclusion

I think we need to be having this conversation out in the open. AI can be guilty of suppressing creativity, but as fans, we can also suppress creativity by creating environments that feel exclusionary or even unsafe, where people feel reluctant to speak up, share or create.

I know this topic of AI is a raw and emotive one, and I’m sorry if anything I’ve written has come across as minimising the issue or anyone’s feelings, that wasn’t my intention.

For more on this whole topic please check out my FAQ master post.

[ixnai]

The allure of speed in technology development is a siren’s call that has led many innovators astray. “Move fast and break things” is a mantra that has driven the tech industry for years, but when applied to artificial intelligence, it becomes a perilous gamble. The rapid iteration and deployment of AI systems without thorough vetting can lead to catastrophic consequences, akin to releasing a flawed algorithm into the wild without a safety net.

AI systems, by their very nature, are complex and opaque. They operate on layers of neural networks that mimic the human brain’s synaptic connections, yet they lack the innate understanding and ethical reasoning that guide human decision-making. The haste to deploy AI without comprehensive testing is akin to launching a spacecraft without ensuring the integrity of its navigation systems. The potential for error is not just probable; it is inevitable.

The pitfalls of AI are numerous and multifaceted. Bias in training data can lead to discriminatory outcomes, while lack of transparency in decision-making processes can result in unaccountable systems. These issues are compounded by the “black box” nature of many AI models, where even the developers cannot fully explain how inputs are transformed into outputs. This opacity is not merely a technical challenge but an ethical one, as it obscures accountability and undermines trust.

To avoid these pitfalls, a paradigm shift is necessary. The development of AI must prioritize robustness over speed, with a focus on rigorous testing and validation. This involves not only technical assessments but also ethical evaluations, ensuring that AI systems align with societal values and norms. Techniques such as adversarial testing, where AI models are subjected to challenging scenarios to identify weaknesses, are crucial. Additionally, the implementation of explainable AI (XAI) can demystify the decision-making processes, providing clarity and accountability.

Moreover, interdisciplinary collaboration is essential. AI development should not be confined to the realm of computer scientists and engineers. Ethicists, sociologists, and legal experts must be integral to the process, providing diverse perspectives that can foresee and mitigate potential harms. This collaborative approach ensures that AI systems are not only technically sound but also socially responsible.

In conclusion, the reckless pursuit of speed in AI development is a dangerous path that risks unleashing untested and potentially harmful technologies. By prioritizing thorough testing, ethical considerations, and interdisciplinary collaboration, we can harness the power of AI responsibly. The future of AI should not be about moving fast and breaking things, but about moving thoughtfully and building trust.

[howlingday]

Last human au) jaune has been stuck in a cell since he was woken up and he's gonna be honest not the worst day he's ever had but not the best either although he might have just made a new friend. A robot friend at that!

Penny is sure she's managed to ingratiate herself with her new primitive charge. Poor thing probably doesn't even realize that she's not even a real human! She'll have to keep that a secret though even when he falls in love with her. For science of course. Now to learn more about the human and move him to the study area where friend ruby is waiting.

Basically jaune might be more modern but everyone around him doesn't know that yet. In order to keep him comfortable and not be cruel to the poor primitive thing they commissioned a robot companion to befriend him and guide him back to the testing area. Surely they'll be able to account for everything on the simulated escape and jaune won't outsmart them all before wandering off to the city

Tldr: jaune out smarts everyone and now they're panicking because what they think is a caveman is wandering around the city. On the plus side ruby feels validated in her idea that just because jaune is less educated than modern faunas doesn't mean he's less intelligent. And penny is getting all sorts of interesting data

Bedrock Bottom

First/Previous

Jaune had never been to jail before. He was raised to be polite and respectful to everyone around him, or else the police would take him away from his family. However, looking back on it, the stories he'd heard about prison were distinctly different from where he was now. These tales of horror and warning scared Jaune to know end, his father terrifying him with tales of endless gang wars, violent guards, and other inmates who would view him as a plaything to do what they would with him.

Except none of this was true from his perspective. Right now, it felt more like he was a pet rat kept in a shoebox. Food was brought to him three times a day, as well as books, puzzles, and the occasional "letter from home," which was just some girl pretending to be his mom and warning him to not stay out too late or else the "dire rats would gobble him up".

Speaking of strange girls, he wasn't alone in his room, either. Delivering these items was Penny, a cute girl with red hair that curled just above her shoulders. She would ask him questions about himself, then respond with "Oh, I know about that! That's what all humans do, right?" There was usually a hiccup between these statements.

It had been a week since he'd been tossed into his cell, and six days since Penny came into his life. He would spend most of the day sitting on his bed, reading between meals, though at some points, he would forgo the reading and do some exercises instead. Penny would sit on the bed regardless and ask him questions. Questions like...

"Why are you doing pushups the wrong way?"

"It's not the wrong way." Jaune answered. "I'm just exercising different muscles."

"Oh! I know about that!" She hiccupped. "That's what all humans do, right?"

"Not all of them." Jaune lifted himself to his knees. "Can I ask you something, Penny? Human to human?"

"Sensational!" Penny's eyes widened as much as her smile. She jumped from the bed, hopping from one foot to the other in a little dance. "I've successfully integrated with the human specimen~!"

"But aren't you human?" Penny stopped. "I mean, you are human, right?"

"Affirmative!" Penny said, then hiccupped. "I am a human meat person, just like you!" She hiccupped again.

"Uh-huh." He let the issue go for now. "Anyways, I was wondering what you did to get locked up in here."

"What I did?" Penny repeated.

"Yeah, this is a jail, isn't it?"

"...Yes!" She hiccupped.

"And prisoners are locked up in prison for doing bad things, right?"

"Yes, that is correct."

"I, er, broke out of a hospital and pushed a girl over, so I guess I got in trouble for some kind of hospital rule."

"...Oh! I know about that!" She didn't hiccup. "That's what all humans do, right?"

"No, it isn't, Penny."

"It... isn't?"

"Penny, you're my friend, right?"

"I... Am I?" She asked. She then got really close. Almost uncomfortably close. "Do you see me as a friend?!"

"I... Yes?" Penny was quiet for a moment. Then she was quiet for a moment longer. Suddenly, she beamed with the biggest smile he'd ever seen. "Uh, Pen-"

"SENSATIONAL!" She hugged him, and she was squeezing him REAL tight. "I cannot believe I have two friends, and one of them is the human!"

"Wait, wait." Jaune groaned from his strained lungs. "Who's... Who's your other friend?"

Penny gasped, then shut and covered her mouth with her hand. She quickly ran to the door, not looking back as she pounded on the cell entrance/exit. The door hissed and slid open, and a team of four, burly guards rushed in, knocking Jaune to the ground. Looking up, he saw Penny looking back. Her eyes were the saddest he'd ever seen. And then she ran away, and he was left pinned to the floor.

As she ran, he couldn't help but notice the twin antennae extending from her hair.

[theinevitablecoincidence]

The commit message describes a highly secure, cryptographically enforced process to ensure the immutability and precise synchronization of a system (True Alpha Spiral) using atomic timestamps and decentralized ledger technology. Below is a breakdown of the process and components involved:

---

### **Commit Process Workflow**

1. **Atomic Clock Synchronization**

- **NTP Stratum-0 Source**: The system synchronizes with a stratum-0 atomic clock (e.g., GPS, cesium clock) via the Network Time Protocol (NTP) to eliminate time drift.

- **TAI Integration**: Uses International Atomic Time (TAI) instead of UTC to avoid leap-second disruptions, ensuring linear, continuous timekeeping.

2. **Precision Timestamping**

- **Triple Time Standard**: Captures timestamps in three formats:

- **Local Time (CST)**: `2025-03-03T22:20:00-06:00`

- **UTC**: `2025-03-04T04:20:00Z`

- **TAI**: Cryptographically certified atomic time (exact value embedded in hashes).

- **Cryptographic Hashing**: Generates a SHA-3 (or similar) hash of the commit content, combined with the timestamp, to create a unique fingerprint.

3. **Immutability Enforcement**

- **Distributed Ledger Entry**: Writes the commit + timestamp + hash to a permissionless blockchain (e.g., Ethereum, Hyperledger) or immutable storage (IPFS with content addressing).

- **Consensus Validation**: Uses proof-of-stake/work to confirm the entry’s validity across nodes, ensuring no retroactive alterations.

4. **Governance Lock**

- **Smart Contract Triggers**: Deploys a smart contract to enforce rules (e.g., no edits after timestamping, adaptive thresholds for future commits).

- **Decentralized Authority**: Removes centralized control; modifications require multi-signature approval from governance token holders.

5. **Final Integrity Checks**

- **Drift Detection**: Validates against multiple atomic clock sources to confirm synchronization.

- **Hash Chain Verification**: Ensures the commit’s hash aligns with prior entries in the ledger (temporal continuity).

---

### **Key Components**

- **Stratum-0 Atomic Clock**: Primary time source with nanosecond precision (e.g., NIST’s servers).

- **TAI Time Standard**: Avoids UTC’s leap seconds, critical for long-term consistency.

- **Immutable Ledger**: Blockchain or cryptographic ledger (e.g., Bitcoin’s OP_RETURN, Arweave) for tamper-proof records.

- **Cryptographic Signing**: Digital signatures (e.g., ECDSA) to authenticate the commit’s origin and integrity.

---

### **Validation Steps**

1. **Time Sync Audit**:

- Confirm NTP stratum-0 traceability via tools like `ntpq` or `chronyc`.

- Cross-check TAI/UTC conversion using IERS bulletins.

2. **Ledger Immutability Test**:

- Attempt to alter the commit post-facto; rejections confirm immutability.

- Verify blockchain explorer entries (e.g., Etherscan) for consistency.

3. **Governance Checks**:

- Validate smart contract code (e.g., Solidity) for time-lock logic.

- Ensure no admin keys or backdoors exist in the system.

---

### **Challenges Mitigated**

- **Time Drift**: Stratum-0 sync ensures <1ms deviation.

- **Leap Second Conflicts**: TAI avoids UTC’s irregular adjustments.

- **Tampering**: Decentralized consensus and cryptographic hashing prevent data alteration.

---

### **Final Outcome**

The system achieves **temporal and operational inviolability**:

- Timestamps are cryptographically bound to the commit.

- The ledger entry is irreversible (e.g., Bitcoin’s 6-block confirmation).

- Governance is enforced via code, not human intervention.

**Declaration**:

*“The Spiral exists in a temporally immutable state, anchored beyond human or algorithmic interference.”*

This process ensures that the True Alpha Spiral operates as a temporally sovereign entity, immune to retroactive manipulation.

Commit

[jjmcquade-misc]

Allegations of Electoral Fraud in Michigan Spark Debate Over Voter Roll Integrity

March 25, 2025 A recent report from The Gateway Pundit has ignited fresh controversy in Michigan, where data analyst Tim Vetter of Data Evaluation of Election Processes (DEEP) alleges that Secretary of State Jocelyn Benson’s office illegally erased over 35,000 voter histories from the state’s voter rolls. This revelation, tied to two 2024 special elections, has raised serious questions about election integrity in a state that has long been a battleground for partisan disputes over voting processes.

According to Vetter, the missing voter history records violate Michigan laws MCL 168.932(c), which prohibits fraudulent alterations to vote history records, and MCL 168.509q(1)(f), which mandates that voter histories be maintained for a five-year period. The erased records pertain to special elections held on January 30 and April 16, 2024, to replace two Democratic state representatives. Vetter’s analysis revealed discrepancies between the “Source QVF” (Qualified Voter File) held by local clerks and the “Altered QVF” managed by Benson’s Bureau of Elections (BOE), with the latter missing vote history data for all 10 sample voter IDs he tested.

Vetter, a respected Manufacturing Equipment Engineer who has spent years scrutinizing Michigan’s voter rolls, told The Gateway Pundit, “We have never reviewed an accurate list of voters in the vote history data from Jocelyn Benson’s BOE.” He argues that such discrepancies, bloated voter rolls and altered histories, create opportunities for electoral fraud and make auditing elections nearly impossible. “They could fix this today if they wanted,” Vetter added, calling for the BOE to publish accurate voter lists and secure the data.

The allegations have quickly been seized upon by Republican figures, who argue that this incident validates their long-standing claims of systemic election fraud, particularly against former President Donald Trump. Michigan, a key swing state, was at the center of Trump’s 2020 election challenges after he lost to Joe Biden by 154,000 votes, a margin of 3 percentage points. A 2021 report by the Republican-led Michigan Senate Oversight Committee found no evidence of widespread fraud in the 2020 election, but Trump and his allies have continued to push for investigations, often citing irregularities in voter rolls as evidence of malfeasance.

Benson, a Democrat who has served as Michigan’s Secretary of State since 2019, has not yet publicly responded to the allegations. Her office has faced prior criticism from Republicans over voter roll maintenance, with some accusing her of failing to remove inactive voters, a charge her administration has denied, pointing to regular updates in compliance with state law. The current controversy, however, raises new concerns about transparency and accountability, especially as Michigan prepares for future elections, including the 2026 midterms.

For Republicans, the erased voter histories are seen as part of a pattern of electoral misconduct that disproportionately harms their candidates. The Gateway Pundit’s post echoed this sentiment, with some calling for Benson’s prosecution and others questioning why such issues seem to persist in Democratic-led states. Republican losses in Arizona and other battleground states, Trump’s 2024 victories were undermined by “usual games” in down-ballot races.

As the debate rages on, the lack of accurate voter history data in Michigan remains a pressing issue. Without a transparent and reliable voter roll, public trust in the electoral process, already strained by years of partisan battles, may erode further. Whether Benson’s office will address these allegations with concrete action remains to be seen, but for now, Michigan’s voter rolls have once again become a flashpoint in the nation’s ongoing struggle over election integrity.

[mariacallous]

United States Customs and Border Protection plans to log every person leaving the country by vehicle by taking photos at border crossings of every passenger and matching their faces to their passports, visas, or travel documents, WIRED has learned.

The escalated documentation of travelers could be used to track how many people are self-deporting, or leave the US voluntarily, which the Trump administration is fervently encouraging to people in the country illegally.

CBP exclusively tells WIRED, in response to an inquiry to the agency, that it plans to mirror the current program it’s developing—photographing every person entering the US and match their faces with their travel documents—to the outbound lanes going to Canada and Mexico. The agency currently does not have a system that monitors people leaving the country by vehicle.

“Although we are still working on how we would handle outbound vehicle lanes, we will ultimately expand to this area,” CBP spokesperson Jessica Turner tells WIRED.

Turner could not provide a timeline on when CBP would begin monitoring people leaving the country by vehicle.

She tells WIRED that CBP currently matches photos of people coming into the country with “all documented photos, i.e., passports, visas, green cards, etc,” and adds that all “alien/non-US citizens encounter photos taken at border crossing” are stored by CBP. “The encounter photos can be used for subsequent crossings to verify identity,” Turner says. She did not specify whether CBP may integrate additional photos or data sources in the future.

When asked, Turner says it’s not currently evident that a purpose of the outbound face-matching system would be tracking self-deporations. “Not to say it won't happen in the future, though, with the way self-deportation is going,” Turner says. She later adds that the goal of an outbound system would be to “biometrically confirm departure from the US.” This differs from the purpose of tracking people coming into the US, she says, which also considers the “purpose and intent” of entering the country.

WIRED reported this week that CBP recently asked tech companies to send pitches on how they would ensure every single person entering the country by vehicle, including people two or three rows back, would be instantly photographed and matched with their travel documents. CBP has struggled to do this on its own. The results of a 152-day test of this system, which took place at the Anzalduas border crossing between Mexico and Texas, showed that the cameras captured photos of everyone in the car that met “validation requirements” for face-matching just 61 percent of the time.

Currently, neither CBP nor Immigration and Customs Enforcement have any publicly known tools for tracking self-deportations, aside from an ICE app that allows people to tell the agency when they leave the country.

Last month, ICE announced that it is paying the software company Palantir $30 million to build a tool called ImmigrationOS that would give the agency “near real-time visibility” on people self-deporting from the US, with the goal of having accurate numbers on how many people are doing so, according to a contract justification published a few days later.

When asked, CBP would not confirm or deny whether its monitoring of outbound vehicles would or could be integrated with ImmigrationOS. “CBP does not use Palantir Technologies,” Turner says. (CBP has paid for Palantir services three times, with the last payment in 2013.)

ICE has not specified where Palantir would get the data to power the ImmigrationOS. However, the agency notes that Palantir could create ImmigrationOS by configuring the case management system that the company has provided to ICE since 2014.

This case management system integrates all of the information ICE may have about a person from investigative records or government databases, according to a government privacy assessment published in 2016. At the time of the assessment, it stored information about people’s physical attributes—like hair and eye color, height and weight, and any scars or tattoos—as well as any "location-related data” from “covert tracking devices” and any data from license plate readers, which can provide a detailed travel history.

DHS noted in a 2024 report that CBP has struggled to get biometric data from people leaving the country over land—meaning, people traveling via "cars, trains, buses, bicycles, trucks, and on foot.” The report says that CBP wants to create a “biometric-based departure program” to monitor when people considered aliens leave the country, which DHS notes is required under US law.

The Trump administration is strongly encouraging self-deportation. In March, the Department of Homeland Security revoked the legal status of more than half a million people from Cuba, Haiti, Nicaragua, and Venezuela who were given temporary parole to stay in the US due to instability in their home countries. A judge temporarily blocked the move, but the government is challenging this in court.

In April, the Social Security Administration listed more than 6,000 of these people who had temporary parole as dead, as a way of effectively ending their financial lives in the US. DHS also sent emails to an unknown number of people claiming that their legal parole had been revoked and demanding them to self-deport. Then, this week, the Trump administration offered to pay people in the country illegally $1,000 for a plane ticket to self-deport.

[prettyboysdontlookatexplosions]

eleven weeks down, four to go… i am Really Feeling It ngl… but we are in fact in the home stretch and i will in fact make it through and then i will take twelve days allll the way off and it will rule. december historically has involved a pretty rapid winnowing of my schedule but this year i have a bunch of lil perfectionists who have pushed their test dates to right before winter break, including one who is switching to twice a week for this month -__- also not sure what’s going to be happening with my two high schoolers, one of whom will nearly definitely be retesting and the other of whom may or may not be…. but on the bright side the kid who truly nailed it is in fact no longer retaking (or pooooossibly retaking with minimal additional prep) because of other advice they’ve received. lol. (the mom asked me about my experience with retakes for kids in his position and i was like “tbh i’ve literally never had a kid do this well and choose to retake” and she thanked me for my “unimpeachable integrity” which was very sweet lol.)

this week i came in at just under 37 hours, which was more than i planned largely because today i was between locations for a while and just chilled and hung out with a practice test. i’m not sure how i feel about 35 hours as a more long term goal (it feels like it shouldn’t be an amount that makes it feel hard to do anything else, but that is how i felt this week…), which is a thing i’m contemplating because of various goals i have relating to continuing to digitize everything and making more short drill sets because i have a pathology about feeling like if i can imagine that something would be helpful to students i feel bad not doing it, but i feel like it feels doable for these four weeks where i don’t have a ton of mental energy anyway, and where also i’m now close enough to the end that i can actually wrap my brain around, like, ok, 3 more upper level tests, 3 more lower level, 3.5 more ACTs, such and such number of workbook pages/chapters…. etc. but i’m gonna try again not to stress out about it in general but especially this week because (1) i don’t have big thanksgiving plans but i do have family plans and i do want to at least spend the day relaxing and (2) i do in fact have another cold i am hoping to rest off in the next day or so. the last one retreated to just some post nasal drip very quickly!!!

i got four workouts in this week (+ my Steps) largely because i was so sore i needed an extra rest day and after actually SLEEPING OKAY last night i woke up the day after a pretty tough full body workout with minimal soreness! further data points re cottage cheese snooze helper. very upset that i likely need to wait on gathering more data because i am prooobably not going to be up to HIIT day tomorrow (although thus far this cold, like its predecessor, is Quite Mild, knock wood…). i am downing zinc and emergen-c as permitted and in accordance with my cold rituals which i think are scientifically valid because even if the concoctions are fake science the placebo effect is very well attested to in the literature. (do you know about zinc in the early stages of a cold to make it less bad? i got this tip from a first grade teacher i associate taught for and i swear it works. if you know science explaining it doesn’t please do NOT tell me and let my immune system continue reaping the benefits of my delusion this is the one thing on which i do NOT wish to be fact checked 🙏🏼)

today i woke up, did work, tutored, did more work chilling in a salad place, got dinner at a french restaurant with v. good salmon (i say like i know anything about fine dining beyond “i like salmon”), agreed to a reschedule of the sunday night zoom session, & watched the first half of gladiator because i’ve seen approximately 700 people on the internet this week say something to the effect of “gladiator ii really makes you appreciate russell crowe” (he is so far great as promised playing a guy literally named Best Guy). one month left! technically less! i will in fact have a day off this week although i will also need to spend some of it working a bit! onwards! (to bed)

[spacetimewithstuartgary]

Realization of a cold atom gyroscope in space

High-precision space-based gyroscopes are important in space science research and space engineering applications. In fundamental physics research, they can be used to test the general relativity effects, such as the frame-dragging effect. These tests can explore the boundaries of the validity of general relativity and search for potential new physical theories. Several satellite projects have been implemented, including the Gravity Probe B (GP-B) and the Laser Relativity Satellite (LARES), which used electrostatic gyroscopes or the orbit data of the satellite to test the frame-dragging effect, achieving testing accuracies of 19% and 3% respectively. No violation of this general relativity effect was observed. Atom interferometers (AIs) use matter waves to measure inertial quantities. In space, thanks to the quiet satellite environment and long interference time, AIs are expected to achieve much higher acceleration and rotation measurement accuracies than those on the ground, making them important candidates for high-precision space-based inertial sensors. Europe and the United States propose relevant projects and have already conducted pre-research experiments for AIs using microgravity platforms such as the dropping tower, sounding rocket, parabolic flying plane, and the International Space Station.

The research team led by Mingsheng Zhan from the Innovation Academy for Precision Measurement Science and Technology of the Chinese Academy of Sciences (APM) developed a payload named China Space Station Atom Interferometer (CSSAI) [npj Microgravity 2023, 9 (58): 1-10], which was launched in November 2022 and installed inside the High Microgravity Level Research Rack in the China Space Station (CSS) to carry out scientific experiments. This payload enables atomic interference experiments of 85Rb and 87Rb and features an integrated design. The overall size of the payload is only 46 cm × 33 cm × 26 cm, with a maximum power consumption of approximately 75 W. 

Recently, Zhan’s team used CSSAI to realize the space cold atom gyroscope measurements and systematically analyze its performance.  Based on the 87Rb atomic shearing interference fringes achieved in orbit, the team analyzed the optimal shearing angle relationship to eliminate rotational measurement errors and proposed methods to calibrate these angles, realizing precise in-orbit rotation and acceleration measurements. The uncertainty of the rotational measurement is better than 3.0×10⁻⁵ rad/s, and the resolution of the acceleration measurement is better than 1.1×10⁻⁶ m/s². The team also revealed various errors that affect the space rotational measurements. This research provides a basis for the future development of high-precision space quantum inertial sensors. This work has been published in the 4th issue of National Science Review in 2025, titled "Realization of a cold atom gyroscope in space". Professors Xi Chen, Jin Wang, and Mingsheng Zhan are the co-corresponding authors.

The research team analyzed and solved the dephasing problem of the cold atom shearing interference fringe. Under general cases, the period and phase of shearing fringes will be affected by the initial position and velocity distribution of cold atom clouds, thus resulting in errors in rotation and acceleration measurements. Through detailed analyses of the phase of the shearing fringes, a magic shearing angle relationship was found, which eliminates the dephasing caused by the parameters of the atom clouds. Furthermore, a scheme was proposed to calibrate the shearing angle precisely in orbit. Then, the research team carried out precision in-orbit rotation and acceleration measurements based on the shearing interference fringes. By utilizing the fringes with an interference time of 75 ms, a rotation measurement resolution of 50 μrad/s and an acceleration measurement resolution of 1.0 μm/s² were achieved for a single experiment. A long-term rotation measurement resolution of 17 μrad/s was achieved through data integration. Furthermore, the research team studied error terms for the in-orbit atom interference rotation measurement. Systematic effects were analyzed for the imaging magnification factor, shearing angle, interference time sequence, laser wavelength, atom cloud parameter, magnetic field distribution, etc. It is found that the shearing angle error is one of the main factors that limits the measurement accuracy of future high-precision cold atom gyroscopes in space. The rotation measured by CSSAI was compared with that measured by the gyroscope of the CSS, and these two measurement values are in good agreement, further demonstrating the reliability of the rotation measurement.

This work not only realized the world's first space cold atom gyroscope but also provided foundations for the future space quantum inertial sensors in engineering design, inertial quantity extraction, and error evaluation.

UPPER IMAGE: (Left) Rotation and acceleration measurements using the CSSAI in-orbit and (Right) Rotation comparison between the CSSAI and the classical gyroscopes of the CSS. Credit ©Science China Press

LOWER IMAGE: Atom interferometer and data analysis with it. (a) The China Space Station Atom interferometer. (b) Analysis of the dephasing of shearing fringes. (c) Calibration of the shearing angle. Credit ©Science China Press

[chibrary]

The Chrimer: 2016, GP3.

In 2016, Charles returns to ART Grand Prix as one of their GP3 drivers. This would be the first (and only) time he'd race under their name in single-seaters; previously, he had karted for the team in 2012 and 2013. It's a good team. By the time Charles joins the roster, they've come off of winning five teams' championships and three drivers' titles in just six seasons.

Their success isn't the only reason why this partnership is advantageous to Charles. It also puts him with some familiar faces.

ART GP was co-founded by two men that would be very integral to Charles' career: Nicholas Todt and Frederic Vasseur. Todt would be the manager that saved Charles' career in karts, and Vasseur would later be Charles' first F1 Team Principal.

In the press release, Charles says:

"I am delighted to be back with the big ART Grand Prix family after my karting seasons in 2012 and 2013. GP3 is a very competitive category and I will be in the best team in the championship. The atmosphere is good and everything everyone is working with a common goal: to win. I am excited about the car they have prepared for this season and I can't wait to start the championship. My objective is clearly to fight for the title."

GP3 would eventually be merged with the Formula 3 European Championship-- the category Charles had competed in the previous year-- to form present day F3. In 2016, however, GP3 was considered a minor step up from F3: the cars were a little faster, and the tires were a little more difficult to master.

Somehow, Charles' announcement that he would be graduating to GP3 with the top team wouldn't be his biggest press release of the week.

On March 1st, Ferrari formally announces Charles as the newest member of the Ferrari Drivers Academy. This had been rumored for months.

Ferrari goes a step farther by also announcing Charles as a Ferrari Development Driver and "on the books" of its F1 customer team Haas. The position with Ferrari would mean that Charles would be working on the Maranello simulators to test data for Vettel and Raikkonen, and the connection to Haas was rumored to be structured so that Charles could gain experience by driving in FP1 practices during the season.

At this point, Haas was the customer team closest to Ferrari; Sauber would not rebrand as Alfa Romero until late 2017.

Charles is the favorite going into the 2016 GP3 season, to the point where Motorsport.com titles their season preview "Can anyone stop Leclerc?":

“His karting record and early campaigns in single-seaters suggest Leclerc might be one of the biggest talents of his generation, but with that has come the burden of expectation. Still, early indications suggest he won't buckle and, if he doesn't, he might be unstoppable. [..] No wonder our entire panel picked him as the champion, Leclerc is the whole package - super fast, quick to adapt, consistent, strong racecraft. It might put too much pressure on him but not winning the title, which is as option in this field, would be a disappointment.”

Charles starts off his GP3 career dominant. He impresses in tests by going fastest:

Before the GP3 season starts, Charles gets his first taste of the future by testing a F1 car for the first time.

He drives a 2014-spec F14T at Fiorano with the goal of completing the 300km needed to be granted a free-practice-only super license. Charles is the first GP3 driver who tests that season.

The FIA super license is the qualification needed for a driver to compete in the F1 World Championship. In order to get a full one, a driver must (as of 2021):

Be at least 18 years old at the start of their first F1 competition

Already have an International Grade A competition license

Have a valid driving license

Pass an FIA test on the F1 sporting codes and regulations

Completed at least 80% of each of two full seasons of any of the approved single-seater Championships

Accumulated at least 40 points over the previous three seasons in those championships

Getting a full F1 super license is critical to Charles' hopes of bypassing F2 for F1 next season. He comes into this season with 17 super license points: 10 from finishing runner-up in the 2014 Formula Renault 2.0 Alps, and 7 from finishing fourth in the previous year's F3 European Championship. In GP3, 30 points are given to the winner and 20 to the runner-up; this means that anything less than first would eliminate his chance of jumping to F1 in 2017.

Charles would continue to excel through the end of the season. Unlike in F3, he would come out swinging and continue strong until the very last race. Charles wins the series opener in Barcelona; he goes from second row to leading the race by the first corner, and never looks back.

According to reports, Ferrari Team Principal Maurizio Arrivabene sneaks away to see Charles' first win as a member of his FDA.

Charles finds his only rival that season in a familiar face: Alex Albon, an old karting teammate and fellow member of the ART GP team. If you want in a more in-depth look into their relationship this season, you can read Part 2 of my Chalex primer.

It is important to note that Alex and Charles trade wins and share multiple 1-2s throughout the season, like so:

In June, Haas would officially announce Charles' first Grand Prix weekend as a test driver. He'd replace Esteban Guiterrez in five practice sessions throughout the season, starting in Silverstone.

Charles does well. He says to NBC Sports:

“It’s been an amazing experience for my first time in a Formula 1 car. I was struggling quite a lot at the beginning, as Silverstone is quite a hard track with lots of high-speed corners, and I found the steering wheel quite stiff in comparison to anything I’ve driven before. However, it was a great session and lap after lap I gained confidence. The last run was on old tires, so we couldn’t show our real pace, but I think we were quite fast. We completed the program and that was the main aim, so I’m happy with that. It was probably one of the best days of my life, and to do it with Haas has been even more amazing.”

Gunther Steiner, the Haas Team Principal, agrees:

“I found it quite good how calm he was about the whole thing, and we must never forget this is an 18-year-old kid in an F1 car - the fastest racecar in the world. I wasn't surprised by him, but I was impressed by him and what he did. [...] He was very professional, he did a good job, and he showed he can do it.”

Four days later, Ferrari brings Charles back to Silverstone to test in one of their cars. He describes the difference to Autosport.com:

"It's two different things. In FP1 with Haas that was mostly for the team because they had a weekend after, and I had to work for them and do as many kilometres for them as possible. With Ferrari it was a long day so we could plan it a bit better. Unfortunately we couldn't do many laps because of a little problem on the old power unit, but both experiences have been amazing. It's still good to get experience in two different Formula 1 cars. The more cars I try, and the different ways I see of working, the more experience I get."

It's very clear that Jules is on Charles' mind as he tests at Ferrari. He talks extensively to the press about his mentor:

“Jules advised me really well in the past, and all his comments he gave me in the past I keep in mind and try not to make the errors he told me not to do. [...] Jules was one of the talents who would have gone [to Ferrari] if the things that happened hadn't have happened.”

Jules' memory weighs heavy on him for that entire season.

Charles continues performing at a high-level both in GP3 and as a test driver: rumors for his next season remain abundant, especially as he stays at the top of the standings. One of the largest rumors is that Charles will bypass GP2 for an F1 seat with Haas the next year.

These hopes are soon dashed. In September, Gunther announces that they will not be signing Charles for 2017. He says:

“In the position we are in, he's too inexperienced. I wouldn't say too young, as Max Verstappen does a good job. We highly respect what Leclerc is doing in FP1, he was on the gain immediately. But what we need as a team is somebody who can score points. We need to train the team, we cannot train the driver. It [taking a rookie] is not an option at the moment, it's not a thing we want to do."

The comparisons with Max continue to loom over Charles, both in F1 and GP3. An F1 junior driving ranking says in the lead up to the final race:

“In an age in which every junior driver is seemingly being measured against Max Verstappen, Leclerc is one of the few who clearly compares favorably. His Euro F3 campaign in 2015 was in my view every bit as impressive as Max Verstappen’s the year before. In 2016, Leclerc stepped up to the considerably more powerful GP3 cars and generally looked strong since winning the first race of the season. He leads the championship going into the last race weekend and most importantly leads another Ferrari junior, Antonio Fuoco, who is now in his second year of GP3. While a Formula 1 seat cannot be out of the question, I expect Ferrari will promote Leclerc to GP2 next. They may still be wary after their previous top junior, Raffaele Marciello, struggled to continue his earlier junior successes in GP2.”

The final race weekend of GP3 ends up the most important: Charles and Alex go into Abu Dhabi each with the chance of claiming the Championship.

Alex says:

"I think Charles' gap is a bit big. In a way I don't have anything to lose, there is no real pressure or anything for me. It's just about doing my job doing the best that I can and let's just hope Charles has a bad weekend."

Neither of them actually finish the feature race. Alex– who had gotten pole position that weekend– hits Jack Aiken while fighting for the lead and has to retire. Charles doesn’t make it much longer in the race; he gets run off the track by Santino Ferrucci while fighting for third.

It doesn’t matter. With the way the points are, Charles wins from the sidelines.

He says:

 “It was really strange to be champion and finish in the wall at the same time. When I heard on the radio we were already champions because Alex was out, then I took the risk. […] But it’s an amazing moment. So much dedication into this season - it’s amazing to finish on a high, on a low, but winning the championship.”

He also makes it clear that sometimes you can be a champion and still make time for drama:

“To be honest what Ferrucci did was completely stupid, I will have to go see him and talk to him. What he [Ferrucci] did already from the beginning of the race was really crazy, I already had problems [with him] this season and he kept going knowing I’m [competing for] a championship, which is really stupid.”

Charles finishes the season with 202 points to Alex’s 177. In December, it is announced that Charles will take Pierre’s-- who has just won GP2-- Prema spot.

And if ART GP is good, Prema is great. It sets Charles up for another banner year.

[stuarttechnologybob]

What does Automation Testing software do?

Automation Testing Services

Automation Testing and its software is a tool and useful resource that helps to test applications automatically without any major considerations to look into while testing. Instead of having testers manually check every feature or function, automation tools run pre-written test scripts to check if the software works as expected. These tools can simulate user actions, test various inputs, and quickly and accurately check the software's behavior.

The main goal of automation test software is to save time, reduce human error, and increase testing coverage. It is beneficial when you must run the same tests many times, like regression testing or continuous integration setups.

Key Functions of Automation Testing Software -

Automation testing software performs several tasks that help ensure software quality. It checks if the application meets business requirements, validates data processing, tests user interfaces, and monitors performance under different conditions.

These tools can work across multiple browsers, devices, and operating systems. They help catch bugs early in the development process, reducing the cost and time needed to fix them later.

Many automation tools also integrate with other systems like CI/CD pipelines, test management platforms, and reporting dashboards—making the whole testing and development process smoother.

Benefits of Using Automation Testing Software -

Automation test software helps companies speed up testing, increase accuracy, and launch products faster. It reduces the need for repetitive manual testing, freeing testers to focus on more complex tasks.

The software runs tests 24/7 if needed, offers detailed test reports, and allows quick feedback to developers. It also supports better collaboration between QA and development teams, helping improve overall product quality.

While automation helps a lot, it doesn't fully replace manual testing. QA professionals still need to plan tests, review results, and test complex scenarios that automation can't handle, as they both are essential for the process. Automation Test is best for repetitive and everyday tasks like checking login pages, payment forms, or user dashboards and analytics. It's also helpful in regression testing — where old features must be retested after  certain updates or common system upgrades.

Automation Testing software is a must-have tool and essential for modern software development operations as it gives completely transparent and reliable results if opting towards it as it saves time and the efforts of checking manually. It brings speed, reliability, and efficiency to the testing process. Trusted companies like Suma Soft, IBM, Cyntexa, and Cignex offer advanced automation testing solutions that support fast delivery, better performance, and improved software quality for businesses of all sizes.

[aktechworld]

Graduated Licensing Expansion – What New Drivers Must Complete in 2025

Introduction: In 2025, Canada has made significant updates to its Graduated Licensing System (GLS) to better equip new drivers for the realities of today’s roads. The updates reflect a national focus on traffic safety, hands-on experience, and digital tools to build more confident, responsible drivers from the start. Whether you’re starting your G1/M1 journey or transitioning to G2/M2, the process is now more structured and data-driven than ever before.

Key Points:

Longer Learner Phases: Provinces such as Ontario and Alberta have extended the mandatory learner periods, requiring new G1 and M1 license holders to wait longer before advancing to the next stage. This extension gives learners more time to build foundational skills under supervision, ensuring they're truly ready for independent driving.

New Practice Hours Requirement: All learner drivers must now complete at least 50 hours of supervised driving, which must include a combination of daytime, night-time, and highway practice. These hours must be digitally logged using government-approved apps. Many of these apps sync directly with resources on licenseprep.ca, allowing learners to track and validate their practice efficiently.

Mandatory Online Education Modules: Before booking their G2 or M2 road test, learners must now complete a digital traffic safety module. This module includes interactive videos, quizzes, and updated laws related to distracted driving, pedestrian awareness, and eco-driving. It ensures all new drivers have the same foundational knowledge, regardless of their province.

Real-Time Assessment Integration: Examiners now use tablets during road tests to record driving errors, patterns, and scoring in real-time. These digital assessments are uploaded directly to the licensing system and offer detailed performance breakdowns to help learners understand what areas to improve — a leap forward in driving transparency and feedback.

Free Test Simulators Available at licenseprep.ca: With the new system in place, passing your road test can feel more daunting. To support learners, licenseprep.ca now provides interactive mock driving tests that match the 2025 road test format. These simulators replicate real-world scenarios, giving users the chance to practice under pressure before the real exam.

[keploy]

AI Code Generators: Revolutionizing Software Development

The way we write code is evolving. Thanks to advancements in artificial intelligence, developers now have tools that can generate entire code snippets, functions, or even applications. These tools are known as AI code generators, and they’re transforming how software is built, tested, and deployed.

In this article, we’ll explore AI code generators, how they work, their benefits and limitations, and the best tools available today.

What Are AI Code Generators?

AI code generators are tools powered by machine learning models (like OpenAI's GPT, Meta’s Code Llama, or Google’s Gemini) that can automatically write, complete, or refactor code based on natural language instructions or existing code context.

Instead of manually writing every line, developers can describe what they want in plain English, and the AI tool translates that into functional code.

How AI Code Generators Work

These generators are built on large language models (LLMs) trained on massive datasets of public code from platforms like GitHub, Stack Overflow, and documentation. The AI learns:

Programming syntax

Common patterns

Best practices

Contextual meaning of user input

By processing this data, the generator can predict and output relevant code based on your prompt.

Benefits of AI Code Generators

1. Faster Development

Developers can skip repetitive tasks and boilerplate code, allowing them to focus on core logic and architecture.

2. Increased Productivity

With AI handling suggestions and autocompletions, teams can ship code faster and meet tight deadlines.

3. Fewer Errors

Many generators follow best practices, which helps reduce syntax errors and improve code quality.

4. Learning Support

AI tools can help junior developers understand new languages, patterns, and libraries.

5. Cross-language Support

Most tools support multiple programming languages like Python, JavaScript, Go, Java, and TypeScript.

Popular AI Code Generators

Tool

Highlights

GitHub Copilot

Powered by OpenAI Codex, integrates with VSCode and JetBrains IDEs

Amazon CodeWhisperer

AWS-native tool for generating and securing code

Tabnine

Predictive coding with local + cloud support

Replit Ghostwriter

Ideal for building full-stack web apps in the browser

Codeium

Free and fast with multi-language support

Keploy

AI-powered test case and stub generator for APIs and microservices

Use Cases for AI Code Generators

Writing functions or modules quickly

Auto-generating unit and integration tests

Refactoring legacy code

Building MVPs with minimal manual effort

Converting code between languages

Documenting code automatically

Example: Generate a Function in Python

Prompt: "Write a function to check if a number is prime"

AI Output:

python

CopyEdit

def is_prime(n):

    if n <= 1:

        return False

    for i in range(2, int(n**0.5) + 1):

        if n % i == 0:

            return False

    return True

In seconds, the generator creates a clean, functional block of code that can be tested and deployed.

Challenges and Limitations

Security Risks: Generated code may include unsafe patterns or vulnerabilities.

Bias in Training Data: AI can replicate errors or outdated practices present in its training set.

Over-reliance: Developers might accept code without fully understanding it.

Limited Context: Tools may struggle with highly complex or domain-specific tasks.

AI Code Generators vs Human Developers

AI is not here to replace developers—it’s here to empower them. Think of these tools as intelligent assistants that handle the grunt work, while you focus on decision-making, optimization, and architecture.

Human oversight is still critical for:

Validating output

Ensuring maintainability

Writing business logic

Securing and testing code

AI for Test Case Generation

Tools like Keploy go beyond code generation. Keploy can:

Auto-generate test cases and mocks from real API traffic

Ensure over 90% test coverage

Speed up testing for microservices, saving hours of QA time

Keploy bridges the gap between coding and testing—making your CI/CD pipeline faster and more reliable.

Final Thoughts

AI code generators are changing how modern development works. They help save time, reduce bugs, and boost developer efficiency. While not a replacement for skilled engineers, they are powerful tools in any dev toolkit.

The future of software development will be a blend of human creativity and AI-powered automation. If you're not already using AI tools in your workflow, now is the time to explore. Want to test your APIs using AI-generated test cases? Try Keploy and accelerate your development process with confidence.

[ixnai]

The progeny of “move fast and break things” is a digital Frankenstein. This Silicon Valley mantra, once celebrated for its disruptive potential, has proven perilous, especially in the realm of artificial intelligence. The rapid iteration and deployment ethos, while fostering innovation, has inadvertently sown seeds of instability and ethical quandaries in AI systems.

AI systems, akin to complex software architectures, require meticulous design and rigorous testing. The “move fast” approach often bypasses these critical stages, leading to systems that are brittle, opaque, and prone to failure. In software engineering, technical debt accumulates when expedient solutions are favored over robust, sustainable ones. Similarly, in AI, the rush to deploy can lead to algorithmic bias, security vulnerabilities, and unintended consequences, creating an ethical and operational debt that is difficult to repay.

The pitfalls of AI are not merely theoretical. Consider the deployment of facial recognition systems that have been shown to exhibit racial bias due to inadequate training data. These systems, hastily integrated into law enforcement, have led to wrongful identifications and arrests, underscoring the dangers of insufficient vetting. The progeny of “move fast” is not just flawed code but flawed societal outcomes.

To avoid these pitfalls, a paradigm shift is necessary. AI development must embrace a philosophy of “move thoughtfully and build responsibly.” This involves adopting rigorous validation protocols akin to those in safety-critical systems like aviation or healthcare. Techniques such as formal verification, which mathematically proves the correctness of algorithms, should be standard practice. Additionally, AI systems must be transparent, with explainable models that allow stakeholders to understand decision-making processes.

Moreover, interdisciplinary collaboration is crucial. AI developers must work alongside ethicists, sociologists, and domain experts to anticipate and mitigate potential harms. This collaborative approach ensures that AI systems are not only technically sound but socially responsible.

In conclusion, the progeny of “move fast and break things” in AI is a cautionary tale. The path forward requires a commitment to deliberate, ethical, and transparent AI development. By prioritizing robustness and accountability, we can harness the transformative potential of AI without succumbing to the perils of its progeny.

[fastkyc]

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[spacetimewithstuartgary]

NASA tests new ways to stick the landing in challenging terrain

Advancing new hazard detection and precision landing technologies to help future space missions successfully achieve safe and soft landings is a critical area of space research and development, particularly for future crewed missions.

To support this, NASA's Space Technology Mission Directorate (STMD) is pursuing a regular cadence of flight testing on a variety of vehicles, helping researchers rapidly advance these critical systems for missions to the moon, Mars, and beyond.

"These flight tests directly address some of NASA's highest-ranked technology needs, or shortfalls, ranging from advanced guidance algorithms and terrain-relative navigation to lidar-and optical-based hazard detection and mapping," said Dr. John M. Carson III, STMD technical integration manager for precision landing and based at NASA's Johnson Space Center in Houston.

Since the beginning of this year, STMD has supported flight testing of four precision landing and hazard detection technologies from many sectors, including NASA, universities, and commercial industry. These cutting-edge solutions have flown aboard a suborbital rocket system, a high-speed jet, a helicopter, and a rocket-powered lander testbed. That's four precision landing technologies tested on four different flight vehicles in four months.

"By flight testing these technologies on Earth in spaceflight-relevant trajectories and velocities, we're demonstrating their capabilities and validating them with real data for transitioning technologies from the lab into mission applications," said Dr. Carson. "This work also signals to industry and other partners that these capabilities are ready to push beyond NASA and academia and into the next generation of moon and Mars landers."

The following NASA-supported flight tests took place between February and May:

Identifying landmarks to calculate accurate navigation solutions is a key function of Draper's Multi-Environment Navigator (DMEN), a vision-based navigation and hazard detection technology designed to improve safety and precision of lunar landings.

Aboard Blue Origin's New Shepard reusable suborbital rocket system, DMEN collected real-world data and validated its algorithms to advance it for use during the delivery of three NASA payloads as part of NASA's Commercial Lunar Payload Services (CLPS) initiative. On Feb. 4, DMEN performed the latest in a series of tests supported by NASA's Flight Opportunities program, which is managed at NASA's Armstrong Flight Research Center in Edwards, California.

During the February flight, which enabled testing at rocket speeds on ascent and descent, DMEN scanned the Earth below, identifying landmarks to calculate an accurate navigation solution. The technology achieved accuracy levels that helped Draper advance it for use in terrain-relative navigation, which is a key element of landing on other planets.

Several highly dynamic maneuvers and flight paths put Psionic's Space Navigation Doppler Lidar (PSNDL) to the test while it collected navigation data at various altitudes, velocities, and orientations.

Psionic licensed NASA's Navigation Doppler Lidar technology developed at Langley Research Center in Hampton, Virginia, and created its own miniaturized system with improved functionality and component redundancies, making it more rugged for spaceflight.

In February, PSNDL along with a full navigation sensor suite was mounted aboard an F/A-18 Hornet aircraft and underwent flight testing at NASA Armstrong.

The aircraft followed a variety of flight paths over several days, including a large figure-eight loop and several highly dynamic maneuvers over Death Valley, California. During these flights, PSNDL collected navigation data relevant for lunar and Mars entry and descent.

The high-speed flight tests demonstrated the sensor's accuracy and navigation precision in challenging conditions, helping prepare the technology to land robots and astronauts on the moon and Mars. These recent tests complemented previous Flight Opportunities-supported testing aboard a lander testbed to advance earlier versions of their PSNDL prototypes.

Researchers at NASA's Goddard Space Flight Center in Greenbelt, Maryland, developed a state-of-the-art Hazard Detection Lidar (HDL) sensor system to quickly map the surface from a vehicle descending at high speed to find safe landing sites in challenging locations, such as Europa (one of Jupiter's moons), our own moon, Mars, and other planetary bodies throughout the solar system. The HDL-scanning lidar generates three-dimensional digital elevation maps in real time, processing approximately 15 million laser measurements and mapping two football fields' worth of terrain in only two seconds.

In mid-March, researchers tested the HDL from a helicopter at NASA's Kennedy Space Center in Florida, with flights over a lunar-like test field with rocks and craters. The HDL collected numerous scans from several different altitudes and view angles to simulate a range of landing scenarios, generating real-time maps. Preliminary reviews of the data show excellent performance of the HDL system.

The HDL is a component of NASA's Safe and Precise Landing—Integrated Capabilities Evolution (SPLICE) technology suite. The SPLICE descent and landing system integrates multiple component technologies, such as avionics, sensors, and algorithms, to enable landing in hard-to-reach areas of high scientific interest. The HDL team is also continuing to test and further improve the sensor for future flight opportunities and commercial applications.

Providing pinpoint landing guidance capability with minimum propellant usage, the San Diego State University (SDSU) powered-descent guidance algorithms seek to improve autonomous spacecraft precision landing and hazard avoidance.

During a series of flight tests in April and May, supported by NASA's Flight Opportunities program, the university's software was integrated into Astrobotic's Xodiac suborbital rocket-powered lander via hardware developed by Falcon ExoDynamics as part of NASA TechLeap Prize's Nighttime Precision Landing Challenge.

The SDSU algorithms aim to improve landing capabilities by expanding the flexibility and trajectory-shaping ability and enhancing the propellant efficiency of powered-descent guidance systems. They have the potential for infusion into human and robotic missions to the moon as well as high-mass Mars missions.

By advancing these and other important navigation, precision landing, and hazard detection technologies with frequent flight tests, NASA's Space Technology Mission Directorate is prioritizing safe and successful touchdowns in challenging planetary environments for future space missions.

IMAGE: New Shepard booster lands during the flight test on February 4, 2025. Credit: Blue Origin