Most of this blog is about AI, but I follow spaceflight for the same reason I follow model releases: it's engineering under hard constraints, done in public, where you can see what works. These six months had three milestones I want to write down properly, plus a few smaller stories. All numbers come from the sources at the bottom.

Artemis II: humans around the Moon again

On 1 April 2026, NASA's Space Launch System launched Orion on Artemis II, the first crewed flight of the Orion spacecraft. The crew: NASA astronauts Reid Wiseman, Victor Glover and Christina Koch, and CSA astronaut Jeremy Hansen.

On 6 April, six days into the mission, they passed the record for the farthest distance humans have travelled from Earth, previously held by Apollo 13 since 1970. NASA's official figure for their farthest point is 252,756 miles (about 406,800 km) from Earth; NASA updated its release on 7 May to reflect the official mileage, with 695,081 miles flown in total.

They splashed down off San Diego on 10 April at 5:07 p.m. PDT, after a nearly ten-day flight.

The heat shield story

The part I found most interesting is the heat shield. After Artemis I in 2022, engineers had found unexpected char loss on Orion's Avcoat heat shield. Instead of only redesigning the shield, NASA changed the re-entry trajectory for Artemis II, shortening the maximum downrange distance so the shield spent less time in the temperature range where the Artemis I problem occurred.

In a 15 October analysis, NASA reported that it worked: Orion entered within 192 feet of its targeted entry interface, splashed down 0.59 miles from the predicted point, and the cracking and char loss were reduced by a factor of ten compared to Artemis I, with nine spots of char loss versus many more on the earlier flight. Peak velocity was about 39 times the speed of sound and external temperatures exceeded 4,000 °F, while the cabin stayed in the mid-70s °F.

I like this as an engineering lesson: sometimes the cheapest fix is changing how you operate a system rather than rebuilding it. It's the same instinct as changing a prompt or a retrieval step before retraining a model.

Blue Origin's setback

Blue Origin had planned to launch its first Blue Moon Mark 1 cargo lander, Endurance, on New Glenn as soon as late summer, carrying NASA science payloads (designated "Moon Base 1" at a NASA event on 26 May). Spaceflight Now reported on 30 June that, after a New Glenn anomaly, the company was working through return-to-flight logistics. It's a reminder that the lunar programme depends on several commercial vehicles all maturing at once.

July: the end of Pegasus and a satellite rescue

On 3 July Northrop Grumman flew the 46th and final Pegasus XL, the air-launched rocket, carrying Katalyst Space Technologies' LINK spacecraft on a NASA-backed mission to capture NASA's Neil Gehrels Swift Observatory and raise it to a higher orbit. The day before, the final Atlas V in the 551 configuration launched Amazon Leo satellites. Small entries in a launch log, but both mark the end of long-running vehicles.

August: the Roman Space Telescope

On 30 August 2026 at 7:26 a.m. EDT, a SpaceX Falcon Heavy launched NASA's Nancy Grace Roman Space Telescope from Launch Complex 39A at Kennedy Space Center. Roman separated from the upper stage at 7:57 a.m. and began a roughly three-month journey to the Sun–Earth L2 point, about a million miles from Earth.

Roman is a dark-energy and dark-matter mission with a field of view SpaceX describes as at least 100 times larger than Hubble's. Reuters called it a roughly $4 billion project and noted its origin as a repurposed spy-satellite telescope. For data people, Roman is interesting because its value is in survey scale: billions of galaxies, and a lot of downstream machine learning.

September: Starship reaches orbit, with a caveat

On 28 September (US time), on its 14th test flight, SpaceX's Starship reached orbit for the first time. NPR reported that the 33-engine Super Heavy booster lifted off from Starbase in Texas, that one of the ship's six engines shut down early, and that SpaceX still decided to go for orbit. Starship orbited Earth twice before splashing down in the Pacific; the planned ten-hour mission was cut to about three hours because of the engine issue.

Reuters reported the same day that the engine failure could complicate NASA's lunar plans, since Starship is one of the two vehicles NASA selected to land astronauts on the Moon. In-space propellant transfer, which a lunar Starship needs, is still to be demonstrated.

What comes next

NASA's Artemis page describes Artemis III, planned for 2027, as a demonstration mission in low Earth orbit to evaluate one or both commercial human landing systems from SpaceX and Blue Origin. The return of astronauts to the lunar surface is now tied to Artemis IV, and NASA anticipates a further surface mission, Artemis V, in 2028. That makes the next year mostly about Starship and Blue Moon: refuelling, reliability and cadence.

What I take from it

The successes of this period come from the same loop: fly, measure, adjust. Artemis II fixed a heat-shield risk with a trajectory change backed by test data; Starship reached orbit on an iterative test campaign and still published a partial failure. That's how I like to build software and AI systems too: small, measurable steps in public, with honest notes about what didn't work.

On the AI side of the same months, see my AI models recap for July–October 2026 and the open-weight models recap. More links: links & repos.

Sources

• NASA: Artemis II mission milestones, image and video recap
• NASA: Artemis II crew eclipses record for farthest human spaceflight
• NASA: NASA welcomes record-setting Artemis II moonfarers back to Earth
• NASA: Rigorous analysis confirms Artemis II heat shield performance (15 October 2026)
• NASA: Artemis programme
• Spaceflight Now: Blue Origin outlines return to flight logistics for New Glenn (30 June 2026)
• Wikipedia: List of spaceflight launches in July–September 2026
• NASA: Nancy Grace Roman Space Telescope launches
• NASA Science: Roman launch
• SpaceX: Roman Space Telescope mission
• Reuters: NASA launches powerful Roman Space Telescope (30 August 2026)
• NPR: SpaceX's Starship reaches orbit for the first time (29 September 2026)
• Reuters: Starship engine failure could impact NASA's Moon mission objectives

1. Walter Isaacson, Elon Musk (2023): 3.5 / 5

I build AI systems for a living, and I advise on the legal side of AI. So I didn't read Walter Isaacson's Elon Musk (2023) as fan fiction or as a takedown. I read it as an engineering case study with a very loud main character. Here is my honest take, with the parts I'd use and the parts I'd leave on the shelf.

What the book is

Isaacson, who also wrote biographies of Steve Jobs, Einstein and Leonardo da Vinci, followed Musk for about two years. He sat in on meetings, read messages and interviewed family, colleagues and critics. The result runs to well over 600 pages, split into 95 short, chronological chapters with plain titles such as "Starlink, 2015–2018". Isaacson himself joked that someone called it a 600-page book for the TikTok generation.

That structure works. Musk runs SpaceX, Tesla, Starlink, Neuralink, The Boring Company, xAI and (from late 2022) Twitter/X in parallel, so the book jumps between Raptor engines, factory floors and server rooms. The short chapters keep it readable, and the engineering scenes are the strongest part of the book.

The story goes from a hard childhood in South Africa, through Zip2 and X.com/PayPal, to the near-bankruptcy years of Tesla and SpaceX in 2008, and ends with the Twitter takeover and the founding of xAI.

Lesson 1: First principles are a tool, not a personality

The idea that comes back most often is Musk's "algorithm" for engineering and manufacturing. Roughly: question every requirement, delete every part or process step you can, then simplify and optimise, then speed up, and only at the end automate. The order matters. Automating a step that should not exist is pure waste.

For AI builders this translates surprisingly well:

• Question the requirement. Does this feature really need an LLM, or would a rule or a database query do?
• Delete before you optimise. Fewer agents, fewer prompts, fewer tools often beat a cleverer orchestration layer.
• Automate last. Run the workflow by hand, understand where it breaks, then build the pipeline.

This is the most useful part of the book for me, and it's worth reading for these chapters alone.

Lesson 2: Speed vs safety is a real trade-off, and the book underplays the cost

Musk's style is a constant "maniacal sense of urgency": impossible deadlines, "surge" weekends, and a high tolerance for things blowing up. For rockets, a test explosion is data. In software it can be the same, because fast iteration beats endless planning.

But AI products don't fail like test rockets on an empty launch pad. They fail on users. Hallucinated legal advice, a biased screening model or a confident driver-assistance claim can hurt people who never agreed to be part of the test.

Several reviewers noticed what the book leaves out here. The New York Review of Books pointed out that Tesla's factory injury rates and the racism lawsuits at the Fremont plant get little or no attention, and that no assembly-line workers appear among the interviews. Others found the safety debate around Autopilot underexplored. As someone who works on AI governance, I'd say this is exactly the part builders need most: who carries the risk when you "move fast"? The book mostly doesn't ask.

My takeaway: copy the iteration speed in your sandbox, and keep it out of production for anything that touches people's rights, health or money.

Lesson 3: Leadership caveats

Isaacson doesn't hide Musk's harsh side. He describes a "demon mode", public humiliation of employees, and impulsive decisions like the mass layoffs and server moves at Twitter. He links some of this to Musk's childhood and his relationship with his father. Several critics, including The Independent, found these bullying scenes repetitive after a while, and I agree.

The bigger criticism, made by The Verge and the Los Angeles Times among others, is that the book stays close to the "great man" story. It explains the bad behaviour as the price of genius, and Isaacson's close access gives Musk plenty of room to tell his own myth. Not every bold promise in the book came true on schedule.

For leaders of AI teams, the honest lesson is narrow. High standards and fast feedback are useful. Fear-based management is not a requirement for great engineering. Plenty of strong teams ship hard things without it.

Pros and cons

Pros

• Unusual access and many first-hand scenes
• Excellent, concrete engineering chapters (the "algorithm", Raptor, factory work)
• Fast, readable structure with short chapters

Cons

• Repetitive in its scenes of conflict and deadlines
• Light on critical analysis, especially in the later Twitter chapters
• Important omissions: worker safety, discrimination claims, Autopilot safety
• Leans towards the "great man" framing

Verdict

Read it as an engineering and decision-making case study, not as a leadership manual. The first-principles material is genuinely useful for anyone building AI systems. The speed-at-all-costs mindset and the management style need a strong filter, and the book won't provide that filter for you.

My rating: 3.5 / 5. Worth reading for builders, as long as you read some of the critical reviews alongside it.

2. Ashlee Vance, Elon Musk (2015): 5 / 5

Before Isaacson there was Ashlee Vance. His Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future (Ecco, 2015) was written before Musk became the most-discussed person on the internet, and that is its strength. Musk first declined to cooperate; Vance kept reporting anyway, interviewed around two hundred people, and Musk eventually agreed to a series of interviews.

The best chapters cover the years when it could all have gone wrong. After failing to buy a launch from Russia, Musk decided SpaceX would build its own rocket. Falcon 1 then failed three times (2006, 2007 and 2008) before the fourth launch reached orbit in September 2008. In the same year Tesla and SpaceX were both nearly out of money, and Musk had to decide whether to split what he had left between them. It reads like a thriller, and it shows first-principles thinking under real pressure.

For anyone building AI systems, the build-versus-buy question is the lasting lesson. Do you own your models and infrastructure, or depend on a vendor? I work on that trade-off every day, in a homelab as well as in client projects. The book is also a reminder that speed without good governance has a price, for employees and for others.

It is dated now (it predates Starlink as a consumer service and xAI), but as an origin story it holds up. My rating: 5 / 5 as an origin story.

Read them together

Vance gives you the near-death years and the origin of the method; Isaacson gives you the method at full scale, and its costs. Read Vance first, then Isaacson with a critical review or two alongside it.