Canonical version: https://thelooplet.com/posts/how-to-build-resilient-space-mission-architecture-lessons-from-2026-deployments

How to Build Resilient Space Mission Architecture: Lessons from 2026 Deployments

TL;DR: 2026’s mix of nuclear‑powered drones, reflector satellites, DSN vulnerabilities, and rapid Starlink launches forces mission architects to adopt modular power, redundant communications, and flexible landing strategies now, or risk single‑point failures.

Introduction: The 2026 Reality Check for Space System Designers

The past twelve months have delivered a stark data point: three independent failures—a Starlink V3 booster ignition abort, a wildfire threatening a Deep Space Network (DSN) ground station, and a delayed nuclear‑driven Titan drone—show that even mature programs still hinge on fragile assets.

• Starlink V3 booster anomaly (TechCrunch, 2026) highlighted how tightly launch schedules are coupled to a single launch vehicle.
• California wildfire (New Scientist, 2026) demonstrated that a ground‑based communications hub can become a single point of failure for deep‑space missions.
• Dragonfly’s delayed RTG delivery (Space Daily, 2026) reminded us that exotic power sources bring unique supply‑chain and integration risks.

At the same time, two emerging technologies are reshaping the design envelope:

Technology	Core Capability	2026 Demonstration
Radio‑isotope Thermoelectric Generator (RTG) on NASA’s Dragonfly	Continuous ~110 W electric power in an environment where solar irradiance < 1 % of Earth’s	First car‑sized nuclear‑powered drone on an extraterrestrial surface
Thin‑film solar reflector (Reflect Orbital’s Earendil‑1)	Space‑based solar concentration delivering multi‑megawatt‑scale irradiance to a ground target	12U CubeSat‑class reflector capable of 2 MW directed flux for 30 min windows

Both push the envelope of power, communication, and autonomy, yet they expose single‑point vulnerabilities that could cripple a mission. The thesis is simple: modern space mission architecture must be built around three pillars—modular power sources, layered communications, and adaptive operations. Ignoring any pillar will cost schedule, budget, and possibly scientific return.

The following sections expand each pillar with concrete implementation guidance, trade‑off analyses, and practical steps that can be adopted today.

1. Modular Power Systems: From RTGs to Sun‑Reflectors

1.1 Why Modularity Matters

A monolithic power bus ties the fate of the entire spacecraft to a single generation technology. If the RTG fails to meet its thermal budget, or a reflector’s deployment mechanism jams, the whole mission can be lost. Modularity decouples generation, storage, and distribution so that each can be swapped, upgraded, or bypassed without redesigning the entire bus.

Benefits

• Risk isolation – Failure in one module does not cascade to others.
• Reuse across missions – A 12U RTG module built for Dragonfly can later power a lunar rover or a Martian atmospheric probe.
• Upgrade path – As higher‑efficiency thermoelectric materials become available, the same mechanical interface can host a next‑generation RTG.

1.2 Nuclear‑Powered Flight on Titan

Dragonfly will carry a General‑Purpose Heat Source (GPHS) RTG delivering ~110 W of continuous electrical power. The design constraints are:

Parameter	Value	Rationale
Electrical output	110 W (steady‑state)	Sufficient for avionics, navigation, and a suite of spectrometers
Mass	~45 kg (Pu‑238)	Drives launch vehicle selection (≥ 1 t to Saturn)
Thermal power	~2 kW	Must be dissipated via a radiative fin array to avoid overheating the drone’s electronics
Lifetime	≥ 14 yr	Matches mission duration plus margin for extended surface operations

Implementation Details

1. Standardized Power‑Module Interface (PMI)
   
   Mechanical: 12U CubeSat form factor (≈ 0.2 m × 0.34 m × 0.5 m) with a kinematic mounting plate and six‑DOF alignment pins.
   
   Electrical: 28 VDC bus, 2 kW thermal interface, and a 100 A current‑limit fuse.

2. Thermal Modeling Workflow

   • Build a 3‑D CAD model of the RTG and surrounding spacecraft bus.
   • Import geometry into ANSYS Fluent; assign Pu‑238 heat source (2 kW) and set radiative boundary conditions for Titan’s ~95 K ambient.
   • Run steady‑state CFD to verify that surface temperatures of adjacent electronics stay below 353 K.
   • Iterate fin geometry or add heat‑pipe links until thermal margins exceed 20 %.

3. Power‑Management Software (PMS)

   • Priority Queue: Use std::priority_queue in C++ to rank payloads (e.g., navigation > science > communications).
   • Dynamic Re‑budgeting: Every 10 s, the PMS reads voltage/current sensors and adjusts duty cycles to keep total draw ≤ 110 W.
   • Fault Isolation: If a subsystem exceeds its allocated budget for three consecutive cycles, the PMS automatically throttles it and logs a fault event to non‑volatile memory.

1.3 Reflective Solar Augmentation

Earendil‑1 demonstrates a complementary approach: a thin‑film reflector that can be re‑oriented to concentrate sunlight onto a target region, acting as a space‑based solar farm.

Spec	Value
Areal density	< 0.1 g cm⁻² (≈ 1 kg for a 10 m² membrane)
Form factor	12U CubeSat (fits with RTG module)
Directed irradiance	Up to 2 MW for 30 min windows
Pointing accuracy	± 0.05° (requires star‑tracker + reaction wheel control)

1. Deployable Membrane Mechanism

   • Actuation: Shape‑memory alloy (SMA) hinges that open the membrane when heated to 70 °C (achieved via resistive heaters).
   • Redundancy: Two independent SMA sets; if one fails, the other can still deploy a 50 %‑area reflector.

2. Control Loop for Beam Steering

   • Sensors: Sun‑sensor array (four photodiodes) + inertial measurement unit (IMU).
   • Algorithm: A proportional‑integral‑derivative (PID) controller runs on a radiation‑hardened microcontroller (e.g., BAE Systems RAD750). The controller updates reaction‑wheel speeds at 10 Hz to maintain pointing.

3. Ground‑Segment Integration

   • Scheduling: Mission Operations Center (MOC) uploads a “reflect‑window” schedule that aligns the reflector’s beam with a ground‑based receiver’s line‑of‑sight.
   • Safety Interlocks: If the beam would intersect an occupied orbital slot, the on‑board software aborts the maneuver and logs a “beam‑collision” event.

1.4 Design Implications for Engineers

• Decouple generation from bus – adopt a Power‑Module Interface (PMI) that standardizes mechanical, electrical, and data connections.
• Implement adaptive power budgeting – deploy a real‑time scheduler that reallocates power based on sensor priorities, similar to the “Ebbinghaus decay engine” used for LLM memory.
• Validate thermal interfaces early – RTG heat dissipation must be modeled with CFD tools (ANSYS Fluent) to avoid overheating sensitive payloads; the same applies to the reflective surface’s thermal gradients when focused sunlight hits a ground target.

Trade‑offs

Option	Mass	Complexity	Reliability	Use‑Case
Stand‑alone RTG (no reflector)	High (45 kg)	Low (no moving parts)	Very high (proven)	Deep‑space, low‑light environments (Titan, Europa)
Reflector‑only (solar)	Low (≈ 1 kg)	High (deployment, pointing)	Moderate (mechanical risk)	Near‑Earth, short‑duration high‑power bursts
Hybrid (RTG + reflector)	Moderate (≈ 46 kg)	Moderate (adds reflector)	High (redundant)	Missions requiring both continuous baseline power and peak‑power bursts (e.g., lunar night operations)

2. Layered Communications: Redundancy from DSN to Starlink

2.1 The Fragility of a Single‑Node Deep‑Space Network

A wildfire in Southern California threatened one of NASA’s three DSN complexes, the very node that relays telemetry from deep‑space probes. The DSN provides ~70 % of the total downlink capacity for missions beyond lunar orbit; loss of a single site can cut bandwidth by up to 25 % and increase round‑trip latency by 15 seconds for Mars missions.

Scenario	Immediate Effect	Cascading Consequence
Site outage (e.g., fire)	Loss of 1‑of‑3 antennas, reduced S‑band/Ka‑band coverage	Longer data download windows → delayed science return
Antenna failure (mechanical)	Reduced gain, higher bit‑error rate	Need for additional error‑correction, higher power consumption on spacecraft
Weather‑related attenuation (tropospheric water vapor)	Temporary link degradation	Potential loss of critical command windows (e.g., trajectory correction)

2.2 Starlink V3 Constellation as a Backup

SpaceX’s recent V3 Starlink launch placed 60 new satellites into a 550 km Sun‑synchronous orbit, but a booster anomaly aborted the second‑stage ignition. Despite the failure, the deployment schedule remains on track, and the constellation now offers 30 Gbps aggregate bandwidth over polar regions—precisely where DSN ground stations are sparse.

• Frequency bands: Ka‑band (27 GHz) for high‑throughput downlink, X‑band (8 GHz) for legacy compatibility.
• Latency: ~30 ms for ground‑to‑satellite, ~150 ms for inter‑satellite laser cross‑links.
• Coverage: Near‑global, with > 95 % availability above 60° latitude.

2.3 Robotic Servicing Satellite (RASS) – On‑Orbit Recovery

Ars Technica highlighted the world’s most advanced robotic servicing satellite, capable of on‑orbit refueling, software uploads, and hardware swaps. RASS can dock with a stranded communications satellite, restore its transponder, or install a backup Ka‑band antenna, effectively turning a single point of failure into a recoverable asset.

Capability	Description
Autonomous rendezvous	Lidar‑based relative navigation, ± 0.1 m accuracy
Modular tool bays	Swappable “plug‑and‑play” tools (e.g., antenna installer, fuel line connector)
On‑board AI	Real‑time fault diagnosis using a lightweight CNN (MobileNetV2)
Secure OTA updates	Post‑quantum Kyber‑1024 encryption for firmware uploads

2.4 Engineering Practices for Resilient Links

2.4.1 Hybrid Ground‑Space Network Topology

1. Deep‑Space Tier – DSN remains primary for > 0.1 AU missions.
2. Near‑Earth Tier – Starlink provides high‑throughput backup for LEO, GEO, and lunar‑orbit missions.
3. Medium‑Range Tier – RASS‑enabled on‑orbit relay nodes fill the “gap” between DSN and Starlink, especially for missions at Lagrange points (e.g., L2) where direct Earth contact is limited.

// Pseudo‑code for a LinkManager API
class LinkManager {
public:
    enum class LinkType { DSN, Starlink, RASS };
    struct LinkStatus { LinkType type; bool healthy; double bandwidth; double latency; };
    LinkStatus selectBestLink();
    void sendTelemetry(const std::vector<uint8_t>& data);
    void receiveCommand(std::vector<uint8_t>& out);
};

The selectBestLink() routine evaluates health, bandwidth, and latency metrics in real time, automatically falling back to the next‑best tier without operator intervention.

2.4.2 Telemetry Buffering

• Circular Buffer Implementation – 256 MiB ring buffer in Rust (VecDeque) stores raw telemetry frames. When a link drops, the buffer continues to accept data; upon reconnection, a “flush” routine streams buffered data in order of acquisition.
• Data Prioritization – Critical housekeeping packets are flagged with a high‑priority bit; the buffer ensures they are transmitted first once the link is restored.

2.4.3 Cross‑Link Encryption

• Algorithm Choice – Kyber‑1024 (post‑quantum) provides ~256‑bit security with modest computational load on radiation‑hardened CPUs.
• Key Management – Each satellite holds a unique public key; the ground segment uses a key‑distribution server to issue session keys for inter‑satellite links. OTA firmware upgrades are signed with a NIST‑approved Ed25519 signature scheme, verified before flash.

2.4.4 Trade‑offs and Decision Matrix

Metric	DSN	Starlink	RASS
Coverage	Deep‑space, limited Earth view	Near‑Earth, polar‑heavy	Flexible, mission‑specific
Latency	5–15 s (light‑time)	30–150 ms (ground‑sat)	0.5–2 s (inter‑sat)
Bandwidth	Up to 500 Mbps (Ka)	Up to 10 Gbps (aggregate)	Up to 1 Gbps (laser cross‑link)
Mass/Power Penalty	Ground‑only	Small antenna on spacecraft (≈ 2 kg)	Additional docking hardware (≈ 15 kg)
Reliability	Proven, but limited nodes	Rapidly expanding, commercial risk	Emerging, high‑cost but high‑value

Guideline: For any mission beyond lunar orbit, design the communications stack to always have at least two independent tiers (e.g., DSN + Starlink). For missions operating at Lagrange points or in cislunar space, include a RASS‑enabled relay as a third tier.

3. In‑Orbit Experimentation: Microgravity Materials and Autonomous Drones

3.1 Crystal Growth on the ISS

Astronauts aboard the ISS grew “otherworldly” crystals by dissolving a supersaturated solution in microgravity, yielding morphologies unattainable on Earth. The experiment required:

• Temperature stability: ± 0.1 °C over 48 h.
• Vibration isolation: Acceleration < 10⁻⁶ g.
• Data logging: High‑resolution (0.01 °C) thermistor readings at 1 Hz.

The resulting crystals exhibited a 12 % higher lattice uniformity, promising for photonic applications.

Implementation Blueprint

1. Thermal Control Loop

   • Sensor: Platinum RTD (0.01 °C resolution).
   • Actuator: Resistive heater (10 W) + Peltier cooler (max 5 W).
   • Controller: PID algorithm tuned in MATLAB/Simulink, then ported to the flight computer (ARM Cortex‑M4).

2. Vibration Isolation Platform

   • Passive: Sorbothane dampers tuned to 0.5–5 Hz.
   • Active: Piezoelectric actuators driven by a feedback loop using accelerometers (ADXL355).

3. Data Provenance

   • UUID: Each crystal batch receives a 128‑bit UUID generated on‑board.
   • Metadata: Sensor logs (temperature, pressure, acceleration) are stored in FITS headers, enabling downstream correlation with lattice measurements.

3.2 Autonomous Navigation on Titan

Dragonfly will navigate using a hybrid SLAM system that fuses inertial measurement unit (IMU) data with stereo vision, compensating for the low‑contrast methane lakes. The onboard processor is a radiation‑hardened FPGA (Xilinx Kintex‑7) running a custom RTOS with deterministic latency < 5 ms for sensor fusion.

Detailed Architecture

Component	Role	Implementation
IMU	Provides high‑rate (200 Hz) angular rate and acceleration data	Space‑qualified MEMS (Honeywell HG4930)
Stereo Cameras	Generates depth maps for obstacle detection	2 MP, 60 ° FOV, HDR mode
SLAM Engine	Fuses IMU & vision to produce pose estimate	EKF‑based fusion, running on Kintex‑7 fabric
Terrain Classifier	Labels ground as “sand”, “rock”, “liquid”	MobileNetV2 quantized to 8‑bit, inference < 2 ms
Path Planner	Generates hop waypoints respecting fuel budget	A* with dynamic cost weighting (terrain difficulty)

Power Budget

Component	Power
FPGA core	5 W
Processor (soft‑core)	3 W
Total	≤ 10 W (well within RTG margin)

3.3 Lessons for System Engineers

• Environmental control as a software problem – use PID loops with adaptive gains to maintain temperature within ± 0.1 °C.
• Edge AI for autonomy – deploy lightweight neural nets (e.g., MobileNetV2 quantized to 8‑bit) on the FPGA to classify terrain types; inference time is under 2 ms.
• Data provenance – tag each crystal batch with a UUID and embed sensor logs in the FITS header; downstream analysis can then correlate morphology with microgravity parameters.

Trade‑offs

Approach	Mass	Power	Complexity	Suitability
Pure IMU dead‑reckoning	Minimal	Low	High drift → frequent corrections	Short hops in feature‑poor terrain
Vision‑only SLAM	Moderate (cameras)	Medium	Sensitive to low contrast	Well‑lit surfaces, not methane lakes
Hybrid IMU+Vision	Moderate	Medium‑High	Best accuracy, higher processing load	General purpose, Titan‑type environments

Recommendation: Adopt the hybrid approach for any mission where visual contrast is unpredictable (e.g., Titan, Europa, Enceladus). Use a fallback IMU‑only mode that triggers when vision fails (e.g., during a dust storm).

4. Landing Site Strategy: Equator vs. South Pole on the Moon

4.1 The Glover Argument

NASA astronaut Victor Glover argues that the lunar equator offers better solar illumination and communications latency than the south‑pole sites currently slated for Artemis IV. Equatorial sites receive > 14 hours of daylight per lunation, reducing reliance on radio‑isotope power units (RPUs).

4.2 Counterpoint: Scientific Value of Polar Volatiles

Polar craters contain water ice deposits crucial for in‑situ resource utilization (ISRU). The south‑pole also offers permanent line‑of‑sight to Earth, simplifying telemetry. However, the trade‑off is a harsher thermal environment, with temperatures swinging between – 173 °C and + 127 °C.

4.3 Decision Framework for Architects

1. Mission Objective Weighting – Assign numeric scores (0‑10) to “resource extraction”, “science return”, and “operational simplicity”. Multiply each by a mission‑specific weighting factor (e.g., 0.4 for ISRU, 0.3 for science, 0.3 for ops).
2. Thermal Budget Modeling – Use NASA’s Thermal Modeling Guide (TMG) to simulate diurnal cycles; a 20 % margin on heater capacity is recommended for polar sites.
   • Toolchain: Thermal Desktop → SINDA/FLUINT → Monte‑Carlo sweeps for albedo variations.
   • Outputs: Minimum/maximum temperatures, heater duty cycle, battery depth‑of‑discharge.
3. Power Architecture Alignment
   • Equatorial Landing → Solar arrays + Li‑ion batteries (baseline).
   • Polar Landing → Compact RTG (e.g., 5 W “Mini‑RTG”) or a reflector‑based solar augmentor (Earendil‑1 style) to supplement limited sunlight.
4. Risk Assessment – Compute failure probability for each power architecture using a fault‑tree analysis (FTA). P_failure = 1 - Π (1 - p_i), where p_i are component failure rates (e.g., solar panel degradation, RTG heat‑pipe leak).

4.4 Practical Example

Parameter	Equatorial Site (e.g., Mare Tranquillitatis)	Polar Site (e.g., Shackleton Crater)
Average solar insolation	1.3 kW m⁻² (14 h/day)	0.1 kW m⁻² (2 h/day)
Peak temperature	+ 120 °C	– 173 °C (night)
Required heater power	0 W (passive)	150 W (continuous)
Power system mass	30 kg (solar + batteries)	45 kg (RTG + batteries)
Communications latency	1.3 s (direct Earth)	1.3 s (direct Earth)
ISRU potential	Low (no ice)	High (water ice)
Overall SiteScore (example weighting)	6.8	7.5

Interpretation – If the mission’s primary driver is ISRU, the polar site wins despite higher thermal risk. If operational simplicity and schedule certainty dominate, the equatorial site is preferable.

5. Team Structure Evolution: Small‑Team Science vs Mega‑Collabs

5.1 Shrinking Solo Papers

Nature reports a rapid decline in one‑ and two‑author papers, with large consortia now accounting for > 80 % of high‑impact publications. The shift reflects the growing complexity of space missions, where hardware, software, and science integration demand multidisciplinary teams.

5.2 Implications for Project Management

Traditional Model	Modern Micro‑service Model
Functional silos (e.g., “Power Team”, “Science Team”) with heavy hand‑offs	Service‑oriented teams each own a well‑defined API (e.g., Power Service, Comm Service, Autonomy Service)
Document‑centric (PDF specs, static drawings)	Code‑centric (design‑by‑contract, version‑controlled Markdown, automated tests)
Monthly milestone reviews	Continuous Integration (CI) pipelines for hardware and software

5.2.1 Micro‑service Style Organization

• Power Service – Owns the PMI, power‑budget software, and thermal models. Exposes PowerAPI (e.g., setMode(), getVoltage()).
• Comm Service – Manages the LinkManager, telemetry buffers, and encryption keys.
• Navigation Service – Provides SLAM and path‑planning APIs.
• Science Service – Handles experiment configuration, data tagging, and downlink packaging.

Each service lives in its own Git repository, with a shared interface contract (OpenAPI or protobuf). Teams can develop, test, and release independently, reducing integration bottlenecks.

5.2.2 Continuous Integration for Hardware

• Hardware‑in‑the‑Loop (HIL) Containers – Use Docker containers that run a full spacecraft simulation (e.g., cFS + NASA‑Core Flight System) alongside a hardware abstraction layer (HAL) that mimics the actual flight board.
• Automated Regression Tests – Every commit triggers a suite that checks: power‑budget compliance, thermal model sanity, communication latency, and SLAM accuracy.
• Gatekeeping – Pull‑request approvals require ≥ 2 engineer sign‑offs and a green CI badge.

5.2.3 Documentation as Code

Store all design documents, requirement matrices, and test procedures in Markdown files within the same repository as the code. Use static site generators (e.g., MkDocs) to publish a live documentation site that updates on each merge. Enforce traceability by linking requirement IDs to code modules via #REQ‑1234 tags.

5.3 Trade‑offs

Aspect	Traditional Large‑Team	Micro‑service Small‑Team
Coordination overhead	High (many meetings)	Low (clear API contracts)
Flexibility	Low (hard to change scope)	High (services can be swapped)
Risk of integration failure	Moderate (late‑stage surprises)	Low (continuous integration catches early)
Initial setup cost	Low (existing org structure)	Higher (tooling, CI pipelines)

Recommendation: Adopt a hybrid approach—maintain a core integration board for overall mission governance, but let subsystem teams operate as independent services with CI‑driven validation. This balances the need for mission‑level oversight with the agility required for rapid response to anomalies.

6. What This Actually Means

The real story is not that new power or communication technologies are “cool”; it is that mission success in 2026 hinges on architectural redundancy. Teams that continue to rely on a single DSN link, a monolithic power bus, or a single‑author scientific approach will incur schedule overruns of 12‑18 months, as the compounded risk of any single failure grows exponentially (risk = 1 − Π(1 − pᵢ)).

Conversely, adopting modular power modules, layered communications, and micro‑service team structures can cut integration risk by an estimated 35 % and enable rapid response to anomalies—critical when a wildfire threatens a ground station or a booster aborts mid‑flight.

Key quantitative insight (derived from NASA’s risk‑assessment spreadsheets, 2026):

• Baseline single‑point architecture: p₁ (RTG failure) ≈ 0.03, p₂ (DSN outage) ≈ 0.07, p₃ (communication satellite failure) ≈ 0.05 → Overall mission failure probability ≈ 0.13 (13 %).
• Redundant architecture (dual‑power, dual‑link, RASS backup): p₁ ≈ 0.03 × 0.2 (backup mitigates 80 %), p₂ ≈ 0.07 × 0.3, p₃ ≈ 0.05 × 0.4 → Overall mission failure probability ≈ 0.03 (3 %).

By 2028, at least 60 % of new deep‑space missions are expected to embed a RASS‑type on‑orbit servicing capability as a baseline requirement, because the cost of a single‑point communications failure will outweigh the added mass and complexity.

7. Key Takeaways

• Power modularity, layered communications, and adaptive operations are non‑negotiable for resilience.
• Adopt a micro‑service team architecture with CI‑driven validation to reduce integration risk.
• Use post‑quantum encryption (Kyber‑1024) and secure OTA updates for both hardware and software.
• Quantify failure probabilities and trade‑offs early; a 20 % thermal margin and a 1 % mass penalty for redundancy can save years of schedule.
• Keep an eye on emerging commercial constellations; they can dramatically alter ground‑segment architecture.

Conclusion

2026 has shown that complexity is no longer a luxury; it is a necessity. The convergence of nuclear‑powered surface explorers, high‑power solar reflectors, and a rapidly evolving commercial communications landscape forces a paradigm shift from monolithic, single‑point designs to modular, redundant, and service‑oriented architectures. By implementing the concrete practices outlined above—standardized power‑module interfaces, hybrid DSN/Starlink/RASS communication tiers, edge AI for autonomy, a data‑driven landing‑site matrix, and a micro‑service team organization—mission designers can dramatically lower the probability of catastrophic failure while preserving the flexibility needed to seize emerging opportunities. The payoff is short development cycles, lower cost, and higher scientific return—qualities that will define the next wave of lunar, Martian, and outer‑planet missions.

Additional Takeaways

• This topic is evolving rapidly—monitor developments closely over the next 6–12 months.
• Evaluate whether existing tooling in your stack already covers this need before adopting new solutions.
• Start with a small proof‑of‑concept before committing to a full implementation.
• Cross‑reference multiple sources before acting on any single vendor claim.
• Share findings with your team—decisions in this area benefit from diverse perspectives.

References

• "Later this decade NASA plans to launch Dragonfly, a car‑sized nuclear‑powered drone…" – Space Daily
• "FCC approves launch of satellite designed to reflect sunlight back to Earth" – The Jerusalem Post
• "See it: NASA astronaut grows otherworldly crystals aboard International Space Station" – Fox Weather
• "Should Artemis IV astronauts actually land near the moon's south pole?" – Space.com
• "Could small‑team science be dying out?" – Nature
• "One of NASA’s crucial links to deep space is at risk of burning down" – New Scientist
• "SpaceX launches new V3 Starlink satellites but suffers another booster failure" – TechCrunch
• "This is the world's most advanced robotic servicing satellite—that we know about" – Ars Technica
• "Context Windows Forget What Matters — I Built a Usage‑Reinforced Decay Engine for AI Agent Memory" – Towards Data Science

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Originally published at The Looplet.